{ "name": "HHS AI use case inventories FY2024 and FY2025, harmonized snapshot", "tracker_version": "1.0.0", "retrieved": "2026-10-30", "sources": { "FY2025": { "omb_repository": "https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory", "omb_commit": "06c7ebeef5b376524211042bd9673b2a7fefd3e3", "omb_file": "Data/2025_individually_reported_AI_use_cases.csv", "omb_file_url": "https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/blob/06c7ebeef5b376524211042bd9673b2a7fefd3e3/Data/2025_individually_reported_AI_use_cases.csv", "omb_raw_url": "https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/06c7ebeef5b376524211042bd9673b2a7fefd3e3/Data/2025_individually_reported_AI_use_cases.csv", "omb_file_sha256": "a62721202ca4b110ce3c53ffdb688d05ad27a35971a7afda4f1179b2493e31d0", "hhs_published_csv": "https://www.hhs.gov/sites/default/files/hhs-ai-use-case-inventory-fy25.csv", "reporting_guidance": "OMB Memorandum M-25-21", "retrieved": "2026-10-30", "hhs_rows_extract": "data/hhs-ai-use-case-tracker/source/omb-fy2025-hhs-rows.csv", "hhs_rows_extract_sha256": "0e4d2511a4d56eb699ee8b56253f85d1df8af7b2f08034c915f57d4b817fe3ab", "hhs_rows": 447 }, "FY2024": { "omb_repository": "https://github.com/ombegov/2024-Federal-AI-Use-Case-Inventory", "omb_commit": "4a29d132291261d4829b187acd8af618e7295294", "omb_file": "data/2024_consolidated_ai_inventory_raw.csv", "omb_file_url": "https://github.com/ombegov/2024-Federal-AI-Use-Case-Inventory/blob/4a29d132291261d4829b187acd8af618e7295294/data/2024_consolidated_ai_inventory_raw.csv", "omb_raw_url": "https://raw.githubusercontent.com/ombegov/2024-Federal-AI-Use-Case-Inventory/4a29d132291261d4829b187acd8af618e7295294/data/2024_consolidated_ai_inventory_raw.csv", "omb_file_sha256": "9a2c9e25d41eb7b4b2e5463459c8ea78e0e02db0c15e82cc09c88e78889b40f5", "hhs_published_csv": "https://www.hhs.gov/sites/default/files/hhs-ai-use-cases-2024-public-inventory.csv", "reporting_guidance": "OMB Memorandum M-24-10", "retrieved": "2026-10-30", "hhs_rows_extract": "data/hhs-ai-use-case-tracker/source/omb-fy2024-hhs-rows.csv", "hhs_rows_extract_sha256": "c13e18bf53ab6043f1a9a04598c03c0beae5c05d7031728dc4a7d3c8e81890b5", "hhs_rows": 271 } }, "summary": { "FY2025": { "total": 447, "by_component": { "NIH": 124, "CDC": 103, "CMS": 73, "FDA": 67, "ACF": 27, "Office of the Secretary": 19, "HRSA": 18, "AHRQ": 8, "ASPR": 4, "SAMHSA": 4 }, "by_stage_as_reported": { "Deployed": 167, "Pre-deployment": 143, "Pilot": 88, "Retired": 49 }, "by_stage_fy2025_equivalent": { "Deployed": 167, "Pilot": 88, "Pre-deployment": 143, "Retired": 49 }, "not_retired": 398, "not_retired_by_component": { "NIH": 104, "CDC": 95, "CMS": 70, "FDA": 67, "Office of the Secretary": 19, "ACF": 18, "HRSA": 18, "ASPR": 4, "AHRQ": 2, "SAMHSA": 1 }, "by_topic_area_as_reported": { "Admin Functions": 117, "Health and Medical": 89, "IT": 87, "Not reported": 49, "Science": 48, "Service Delivery": 18, "Other": 11, "Emergency Mgmt": 8, "Procurement and Finance Mgmt": 7, "Benefits Processing": 5, "Law Enforcement": 4, "HR": 3, "Cybersecurity": 1 }, "by_impact_designation": { "Not high-impact": 432, "Presumed high-impact, determined not high-impact": 13, "High-impact": 2 }, "by_ai_type_as_reported": { "Generative AI": 140, "NLP": 117, "Classical ML": 87, "Not reported": 49, "Agentic AI": 29, "Computer Vision": 17, "Other": 8 }, "public_health_tagged": 72, "public_health_tagged_not_retired": 71, "by_public_health_function": { "surveillance": 22, "forecasting": 4, "laboratory": 16, "product-safety": 21, "environmental-occupational": 10, "emergency": 2, "program-integrity": 7, "communication": 1 }, "public_health_tagged_by_component": { "CDC": 40, "FDA": 21, "CMS": 5, "NIH": 3, "ACF": 1, "Office of the Secretary": 1, "ASPR": 1 }, "names_also_in_fy2024": 145 }, "FY2024": { "total": 271, "by_component": { "NIH": 82, "CDC": 55, "CMS": 41, "FDA": 27, "ACF": 15, "HHS (unspecified)": 15, "ASPR": 12, "Office of the Secretary": 8, "AHRQ": 6, "HRSA": 6, "SAMHSA": 4 }, "by_stage_as_reported": { "Operation and Maintenance": 104, "Initiated": 59, "Acquisition and/or Development": 57, "Implementation and Assessment": 35, "Retired": 16 }, "by_stage_fy2025_equivalent": { "Deployed": 104, "Pilot": 35, "Pre-deployment": 116, "Retired": 16 }, "not_retired": 255, "not_retired_by_component": { "NIH": 76, "CDC": 48, "CMS": 40, "FDA": 27, "HHS (unspecified)": 15, "ACF": 13, "ASPR": 12, "Office of the Secretary": 8, "AHRQ": 6, "HRSA": 6, "SAMHSA": 4 }, "by_topic_area_as_reported": { "Mission-Enabling (internal agency support)": 133, "Government Services (includes Benefits and Service Delivery)": 74, "Health & Medical": 51, "Law & Justice": 6, "Emergency Management": 4, "Education & Workforce": 3 }, "by_impact_designation": { "Neither rights- nor safety-impacting": 267, "Safety-impacting": 2, "Rights-impacting": 2 }, "by_ai_type_as_reported": { "Not reported": 271 }, "public_health_tagged": 44, "public_health_tagged_not_retired": 42, "by_public_health_function": { "surveillance": 18, "forecasting": 2, "laboratory": 4, "product-safety": 10, "environmental-occupational": 10, "emergency": 5, "program-integrity": 4, "communication": 0 }, "public_health_tagged_by_component": { "CDC": 19, "FDA": 10, "NIH": 7, "CMS": 5, "ASPR": 3 } } }, "use_cases": [ { "tracker_id": "hhs-2025-05246a872a", "fiscal_year": "FY2025", "use_case_name": "Design Your Facility", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-5aa96b2d2b", "fiscal_year": "FY2025", "use_case_name": "Discover Financial Business Intelligence System (FBIS) Report Analysis (Sub-CAN Line Items)", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Procurement and Finance Mgmt", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "How can spend plans be more real-time and less manual to create and keep up to date? The data used to create and monitor spend plans are spread across multiple out-of-the-box reports available in FBIS. Combing through them is time-intensive, making it more difficult to keep spend plans up to date in real-time. This task is especially challenging due to the unstandardized nature of sub-CAN (Congressional Appropriation Number) line item descriptions." }, { "tracker_id": "hhs-2025-422ac80c24", "fiscal_year": "FY2025", "use_case_name": "Grant Spend Health Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-4ea2317def", "fiscal_year": "FY2025", "use_case_name": "Structuring and Validating Completeness of Case Data Information", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "Benefits Processing", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "How can referrals from the Department of Homeland Security (DHS) be more quickly reviewed for critical pieces of information about their parents and the reason for separation? Since November 2024, when a minor is separated from their parent or legal guardian, the U.S. Customs and Border Protection (CBP) within DHS is required to send certain pieces of information about the parent/legal guardian in accordance with the Ms L vs ICE settlement. CBP send this information in a block of free text and sometimes do not include the required information. Historically, ACF's Office of Refugee Resettlement (ORR)’s tracking of required information has been done manually and inconsistently." }, { "tracker_id": "hhs-2025-2738568eda", "fiscal_year": "FY2025", "use_case_name": "Structuring Notice of Concern Data", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "Benefits Processing", "ai_type_as_reported": "Computer Vision", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "How can the Office of Refugee Resettlement (ORR) clear its backlog of notices of concern (NOC) and minimize backlog in the future? Notice of Concern (NOC) forms contain critical information regarding safety of children who have left ORR's care. Some forms are received as scans, with the information not in machine-readable format. ORR receives hundreds of NOCs a day. Due to personnel shortage in the Prevention of Child Abuse and Neglect Team (PCAN) team responsible for reviewing and acting on NOCs, as of October 2024 there was a backlog of over 30,000 NOCs." }, { "tracker_id": "hhs-2025-7300f57d39", "fiscal_year": "FY2025", "use_case_name": "Unaccompanied Children Program Policy & Procedure Research Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "How can the Office of Refugee Resettlement (ORR) research the laws, standards, policies, and procedures applicable to monitoring visits more quickly while maintaining thoroughness? The Office of Refugee Resettlement (ORR) conducts monitoring visits at least monthly to ensure that care providers meet minimum standards for the care and timely release of unaccompanied children, and that they abide by all Federal and State laws and regulations, licensing and accreditation standards, ORR policies and procedures, and child welfare standards. If ORR monitoring finds a care provider to be out of compliance with requirements, ORR issues corrective action findings and requires the care provider to resolve the issue within a specified time frame. Compliance determination involves research into the various laws, standards, policies, and procedures." }, { "tracker_id": "hhs-2025-913fb0680b", "fiscal_year": "FY2025", "use_case_name": "Unaccompanied Children Process Model Digital Twins", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-5fce7d00b2", "fiscal_year": "FY2025", "use_case_name": "Analyzing Public Comments on Proposed Rule", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-b0b92b6e3b", "fiscal_year": "FY2025", "use_case_name": "Outreach List Segmentation", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-6b682f2857", "fiscal_year": "FY2025", "use_case_name": "Ask HR Policy", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "HR", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "How can ACF better support Executive Officers and Administrative Officers find relevant Human Resources (HR) policy? HHS has over 70 HR policies that ACF Executive Officers/Administrative Officers must navigate when trying to find an answer to a question." }, { "tracker_id": "hhs-2025-be3e63ffda", "fiscal_year": "FY2025", "use_case_name": "Child Welfare Information Automated Inquiry System", "previous_name_reported": "Child Welfare Information Gateway OneReach Application", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "How can Child Welfare Information Hotline callers get the right information faster without increasing staffing? The Children's Bureau runs the Children Welfare Information Gateway, a connection to trusted resources on the child welfare continuum. The Information Gateway has a hotline for answering questions or requesting information: https://www.childwelfare.gov/stay-connected/contact/. Callers to the hotline range from those having more routine questions (such as asking for the contact information for their state's child welfare agency) to reporting more complex, nuanced situations." }, { "tracker_id": "hhs-2025-f7ad8a9da5", "fiscal_year": "FY2025", "use_case_name": "Collective Bargaining Compass", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "HR", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "How can the HHS-NTEU collective bargaining agreement be more easily referenced? The HHS NTEU Collective Bargaining Agreement and associated rules are over 400 pages long, covering numerous topics related to employer-labor relations." }, { "tracker_id": "hhs-2025-4fb9e3ff99", "fiscal_year": "FY2025", "use_case_name": "Discover User Assistant", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "How can ACF Discover users better understand how to use the platform? ACF Discover is a relatively new platform designed to streamline various analyses and data maintenance responsibilities of ACF Executive Officers and other ACF administrative and management staff. When launched, ACF Discover users were trained with instructional documentation. However, some users still find it difficult to understand the different software modules and how to use them." }, { "tracker_id": "hhs-2025-7760a5b1ad", "fiscal_year": "FY2025", "use_case_name": "Policy Knowledge Base Data Migration", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-e9f737dbd0", "fiscal_year": "FY2025", "use_case_name": "Qualitative Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Other", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "How can thematic coding and trend analysis across qualitative data be done more efficiently? ACF staff often conduct surveys and interviews, which generate qualitative data that needs to be analyzed for themes and trends. The standard approach involves multiple human passes of labeling the data for analysis, which is very time-intensive." }, { "tracker_id": "hhs-2025-00e9e35c33", "fiscal_year": "FY2025", "use_case_name": "Intakes Referral Parser", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-9435dd43a9", "fiscal_year": "FY2025", "use_case_name": "Training and Technical Assistance (TTA) GenAI Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-df8ee37269", "fiscal_year": "FY2025", "use_case_name": "Funding Opportunity Redundancy Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-ddf83f4c22", "fiscal_year": "FY2025", "use_case_name": "Unaccompanied Child Sponsor Identity Verification", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "High-impact", "topic_area_as_reported": "Benefits Processing", "ai_type_as_reported": "Computer Vision", "public_health_functions": [ "program-integrity" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "How can ACF strengthen sponsor identity verification to reduce fraudulent sponsor applications? Throughout the process of sponsor vetting, there are various touchpoints where the identity of the sponsor is critical to ensuring a child will be placed with a safe guardian. Knowing that the person (sponsor, household adult) is who they claim to be and that the person presenting at different points of the sponsor application process is consistently the person who was vetted is essential to ensure the welfare of a child." }, { "tracker_id": "hhs-2025-e1ae6900af", "fiscal_year": "FY2025", "use_case_name": "Head Start Correspondence Categorizer", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "How can regional management and program specialists with large grant workloads keep track of all the correspondence being received by staff in their regional offices? The Office of Head Start's (OHS) program specialists receive correspondence through the Head Start Enterprise System (HSES) for a variety of topics. Many of the requests, questions, and reports are tracked to completion in another system that has more robust alerting and workflow management capabilities. OHS is automating the data transfer between these two systems, introducing a data processing step that helps categorize correspondence." }, { "tracker_id": "hhs-2025-36be824e97", "fiscal_year": "FY2025", "use_case_name": "Builder Buddy", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "How can ACF staff more readily develop their own tailored virtual assistants on our enterprise genAI platform? ACF's enterprise generative AI platform gives users the tools to create their own tailored virtual \"agents\" to assist with more specialized tasks specific to a user's work. Users do not need to code to build these virtual assistants but do have to provide adequate context and instructions. Builder Buddy lets users draft a virtual assistant through describing their needs and providing context in a natural conversational manner as an alternative to a form-based builder interface." }, { "tracker_id": "hhs-2025-c6679d4173", "fiscal_year": "FY2025", "use_case_name": "Document Review for Alignment with Executive Orders: Position Descriptions", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "HR", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "How can ACF identify position descriptions that may need to be adjusted for alignment with recent executive orders? ACF needed to conduct an audit across position descriptions in accordance with HHS Secretarial Directives related to recent executive orders such as Executive Order 14151 \"Ending Radical and Wasteful Government DEI Programs and Preferencing\" and Executive Order 14168 \"Defending Women From Gender Ideology Extremism and Restoring Biological Truth to the Federal Government\"." }, { "tracker_id": "hhs-2025-3ee039ec7f", "fiscal_year": "FY2025", "use_case_name": "Document Review for Alignment with Executive Orders: Grant Materials", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Other", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "How can ACF identify grants that may need to be reviewed for alignment with recent executive orders? ACF needed to conduct an audit across existing grants and new grant applications in accordance with HHS Secretarial Directives related to recent executive orders such as Executive Order 14151 \"Ending Radical and Wasteful Government DEI Programs and Preferencing\" and Executive Order 14168 \"Defending Women From Gender Ideology Extremism and Restoring Biological Truth to the Federal Government\"." }, { "tracker_id": "hhs-2025-cdeb830484", "fiscal_year": "FY2025", "use_case_name": "Grant management support: structuring information in applications", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Other", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "How can ACF staff more efficiently review grant applications that have non-standardized formats? Grant applications for ACF-funded programs can come in different formats, even when a standard set of questions or template is provided. ACF staff evaluating applications look for explanation and details to assess against pre-established evaluation criteria. Going back-and-forth across an application to find the relevant explanation can be time intensive, especially for applications that include multiple documents spanning 50+ pages." }, { "tracker_id": "hhs-2025-d708ca07a1", "fiscal_year": "FY2025", "use_case_name": "Acquisition support: co-drafting acquisition packages", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Procurement and Finance Mgmt", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "How can ACF acquisition teams more efficiently draft acquisition packages? In addition to tailored performance work statements (PWS), acquisition packages include multiple documents that often require information based on the PWS. There are also instances where a recompete is issued that largely follows a previous contract, with some updates to volumes. Different contract awarding agencies have different formats. ACF's acquisition teams therefore commonly need to repackage information." }, { "tracker_id": "hhs-2025-62990354af", "fiscal_year": "FY2025", "use_case_name": "Acquisition support: assisting reviews and co-drafting technical evaluation documents", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Procurement and Finance Mgmt", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "How can ACF teams more efficiently review contract proposals and summarize technical evaluation discussions? In response to Requests for Information and Requests for Proposals, ACF teams receive responses from interested vendors. In the review process, ACF staff need to provide summarized comments for each response on potential suitability for delivering the work. These summaries are based on individual and group review against pre-established criteria. When there is a high volume of vendor responses, review teams have many summaries to write." }, { "tracker_id": "hhs-2025-2be966b93b", "fiscal_year": "FY2025", "use_case_name": "Sub Can Line Finder", "previous_name_reported": "", "bureau_as_reported": "HHS/ACF/OA", "component": "ACF", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The LLM helps compile an aggregated report of costs by intaking a specific CAN, category, sub CAN line item description, and a projection cost from the user (either by a CSV or manual entry) and returning line items from the open-closed report and requisition purchase order report related to the sub CAN line item description such as supplier name, document number, document type, and total obligations for that sub CAN line item." }, { "tracker_id": "hhs-2025-8199312a18", "fiscal_year": "FY2025", "use_case_name": "USPSTF Public Forms Data AI Integration", "previous_name_reported": "", "bureau_as_reported": "HHS/AHRQ/CEPI", "component": "AHRQ", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-49a232225e", "fiscal_year": "FY2025", "use_case_name": "AI DevOps - Improving Development and CI/CD Operations", "previous_name_reported": "", "bureau_as_reported": "HHS/AHRQ/CFACT", "component": "AHRQ", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Introducing AI to DevOps can identify and reduce errors, shorten release cycles, and empower development teams with data-driven insights, resulting in faster continuous integration and shorter development lifecycles." }, { "tracker_id": "hhs-2025-a7c820a2a4", "fiscal_year": "FY2025", "use_case_name": "AHRQ Search", "previous_name_reported": "", "bureau_as_reported": "HHS/AHRQ/CQuIPS", "component": "AHRQ", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Organization wide search that includes Relevancy Tailoring, Auto-generation Synonyms, Automated Suggestions, Suggested Related Content, Auto Tagging, and \"Did you mean?\" to allow visitors to find specific content. This AI use case enhances our agency's efficiency and user experience by optimizing search results, auto-completing queries, suggesting relevant searches and content tags, as well as proposing spelling corrections." }, { "tracker_id": "hhs-2025-21b16fe4dc", "fiscal_year": "FY2025", "use_case_name": "Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/AHRQ/CQuIPS", "component": "AHRQ", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-e500d65420", "fiscal_year": "FY2025", "use_case_name": "Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/AHRQ/CQuIPS", "component": "AHRQ", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-cad63383df", "fiscal_year": "FY2025", "use_case_name": "NLQuery- As Data or Pulse", "previous_name_reported": "", "bureau_as_reported": "HHS/AHRQ/CQuIPS", "component": "AHRQ", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-b705bf4fac", "fiscal_year": "FY2025", "use_case_name": "Quality and Safety Review System AI-enabled automated abstraction", "previous_name_reported": "", "bureau_as_reported": "HHS/AHRQ/CQuIPS", "component": "AHRQ", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-c7cc113d74", "fiscal_year": "FY2025", "use_case_name": "Enhancing Diversity in Peer Review - Pilot", "previous_name_reported": "", "bureau_as_reported": "HHS/AHRQ/OEREP", "component": "AHRQ", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-a1ff0e0ea8", "fiscal_year": "FY2025", "use_case_name": "PRISM Ally", "previous_name_reported": "", "bureau_as_reported": "HHS/ASFR/OA", "component": "Office of the Secretary", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Procurement and Finance Mgmt", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Assists users with federal regulatory and agency policy questions related to acquisition" }, { "tracker_id": "hhs-2025-6783ee037e", "fiscal_year": "FY2025", "use_case_name": "GrantSolutions Text Analyzer Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/ASFR/OG", "component": "Office of the Secretary", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Program staff authoring NOFOs need capabilities to simplify language and ensure NOFOs remain compliant with the Plain Writing Act of 2010." }, { "tracker_id": "hhs-2025-58f35600d6", "fiscal_year": "FY2025", "use_case_name": "GrantSolutions AI Writing Assistant", "previous_name_reported": "", "bureau_as_reported": "HHS/ASFR/OG", "component": "Office of the Secretary", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Program staff authoring NOFOs need capabilities to simplify language and ensure NOFOs remain compliant with the Plain Writing Act of 2010." }, { "tracker_id": "hhs-2025-4376f2aa21", "fiscal_year": "FY2025", "use_case_name": "GrantSolutions Recipient Risk Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/ASFR/OG", "component": "Office of the Secretary", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "program-integrity" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Grant managers need an efficient method to conduct risk assessments before issuing financial assistance awards to prevent fraud, waste, and abuse." }, { "tracker_id": "hhs-2025-df50fc41fd", "fiscal_year": "FY2025", "use_case_name": "GrantSolutions Non?Competing Continuation Approval Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/ASFR/OG", "component": "Office of the Secretary", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Grant managers need an efficient workflow to identify and analyze differences in non-competing continuation budgets and narratives from year to year." }, { "tracker_id": "hhs-2025-03acd64d62", "fiscal_year": "FY2025", "use_case_name": "GrantSolutions Helpdesk Agent", "previous_name_reported": "", "bureau_as_reported": "HHS/ASFR/OG", "component": "Office of the Secretary", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "GrantSolutions users need an efficient method to get answers to their login and access-related questions." }, { "tracker_id": "hhs-2025-2e08f1b62e", "fiscal_year": "FY2025", "use_case_name": "GrantSolutions Non?Competing Continuation Review Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/ASFR/OG", "component": "Office of the Secretary", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Grant managers need an efficient workflow to identify and analyze differences in non-competing continuation budgets and narratives from year to year and ensure narratives promote federal priorities and mission." }, { "tracker_id": "hhs-2025-ea7c9fbefd", "fiscal_year": "FY2025", "use_case_name": "Senior Leadership Briefing Generation", "previous_name_reported": "", "bureau_as_reported": "HHS/ASPR/CP", "component": "ASPR", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Emergency Mgmt", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The manual process of parsing and understanding documents, and summarizing their content" }, { "tracker_id": "hhs-2025-83e7e9d24d", "fiscal_year": "FY2025", "use_case_name": "AIP Cyber Incident Ingestion", "previous_name_reported": "", "bureau_as_reported": "HHS/ASPR/CP", "component": "ASPR", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Cybersecurity", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The manual process of parsing out cyber incidents and entering them into the system" }, { "tracker_id": "hhs-2025-a375efc952", "fiscal_year": "FY2025", "use_case_name": "ASPR TRACIE - Web search results improvement (prototyping stage)", "previous_name_reported": "", "bureau_as_reported": "HHS/ASPR/CP", "component": "ASPR", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Web search results improvement - ASPR TRACIE" }, { "tracker_id": "hhs-2025-6684c2e8b1", "fiscal_year": "FY2025", "use_case_name": "emPOWER AI", "previous_name_reported": "", "bureau_as_reported": "HHS/ASPR/ODAIA", "component": "ASPR", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "emergency" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Public health authorities, first responders and others across the emergency management spectrum indicated that they had to be able to more rapidly access HHS emPOWER Map publicly available data, particularly in unstable internet conditions, during disasters. emPOWER AI, an Amazon Alexa Skill, was created to allow anyone with a smartphone, particularly emergency and first responders, to be able to request the data from the HHS emPOWER Map and receive it within seconds from the field to headquarters." }, { "tracker_id": "hhs-2025-85a0380f43", "fiscal_year": "FY2025", "use_case_name": "Certification and Testing/Program Administration – AI-enabled Internal Processes", "previous_name_reported": "", "bureau_as_reported": "HHS/ASTP", "component": "Office of the Secretary", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "ONC’s certification and related program operations rely on many manual, text-heavy, and repetitive tasks (e.g., analyzing surveillance reports, validating certification test results, generating public meeting materials, preparing communications, managing Jira tickets, summarizing standards/IG guidance, and drafting acquisition documents). These activities are time-consuming, error-prone, and difficult to scale as program workload increases. The AI use case is intended to automate or semi-automate routine document drafting, data summarization, basic analysis, and information retrieval across these functions so staff can focus on higher-value review, oversight, and decision-making." }, { "tracker_id": "hhs-2025-d3d4c5bf9a", "fiscal_year": "FY2025", "use_case_name": "The NEF DSI/AI project addresses local validation of AI-based clinical decision support (CDS)/decision support interventions (DSI) in provider settings.", "previous_name_reported": "", "bureau_as_reported": "HHS/ASTP", "component": "Office of the Secretary", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Other", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Assess quality of AI-based clinical decision support (CDS) tools" }, { "tracker_id": "hhs-2025-3656230ca0", "fiscal_year": "FY2025", "use_case_name": "HaMLET: Harnessing Machine Learning to Eliminate Tuberculosis", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-a919b1e3cd", "fiscal_year": "FY2025", "use_case_name": "Adverse Childhood Experiences (ACEs) Literature Review Dashboard", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-af0f0d1855", "fiscal_year": "FY2025", "use_case_name": "Automated Analysis of Injury Control Research Center (ICRC) Annual Progress Reports (APRs) using Large Language Models", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "The AI is designed to streamline the review process of Annual Progress Reports (APRs) submitted by Injury Control Research Centers (ICRCs), improve efficiency, and support the evaluation of the performance and progress of ICRC-funded activities." }, { "tracker_id": "hhs-2025-c8d9e6da94", "fiscal_year": "FY2025", "use_case_name": "Detecting Stimulant and Opioid Misuse and Illicit Use", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To detect and analyze non-therapeutic (illicit or misuse) stimulant and opioid use from free-text clinical notes in EHRs, which is not possible using standard medical codes." }, { "tracker_id": "hhs-2025-120dc5a1b3", "fiscal_year": "FY2025", "use_case_name": "DHP Virtual Assistant", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To assist HIV researchers by retrieving relevant information related to HIV and HIV research, improving productivity and efficiency in literature review." }, { "tracker_id": "hhs-2025-357ef0bbe3", "fiscal_year": "FY2025", "use_case_name": "Fuzzy matching tool", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To improve clearance and review management of CDC publications by identifying similar records between eClearance submissions and CDC-authored publications, ensuring compliance with NIHMS and CDC public access policies, and supporting science prioritization and impact analyses." }, { "tracker_id": "hhs-2025-51d2947438", "fiscal_year": "FY2025", "use_case_name": "Reviewing Global Influenza Vaccine Literature", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "To efficiently review a large volume of published literature and identify abstracts related to access to influenza vaccines." }, { "tracker_id": "hhs-2025-1386d787a0", "fiscal_year": "FY2025", "use_case_name": "HIV Data Virtual Assistant", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To improve the data retrieval and automation process for HIV researchers by providing information from datasets and generating code for data analysis, thereby alleviating challenges in finding appropriate HIV-related data." }, { "tracker_id": "hhs-2025-276054e192", "fiscal_year": "FY2025", "use_case_name": "Identify infrastructure supports for physical activity (e.g. sidewalks) in satellite and roadway images", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Computer Vision", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To streamline and automate the surveillance of sidewalks and other infrastructure that support physical activity, reducing the labor and cost associated with manual inspection." }, { "tracker_id": "hhs-2025-928eb8ac05", "fiscal_year": "FY2025", "use_case_name": "Immunization Information Systems Guidance Documentation Navigation and Management (IDAB EDAV Azure OpenAI Technology Use)", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To provide a modernized, efficient, and user-friendly method for CDC staff to retrieve, interact with, and update Immunization Information Systems (IIS) guidance documentation, improving knowledge retrieval and supporting the creation and validation of new guidance documents." }, { "tracker_id": "hhs-2025-b282827cea", "fiscal_year": "FY2025", "use_case_name": "LaserAI", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To reduce screening time and improve accuracy in all phases of systematic reviews by automating title and abstract screening, PDF retrieval, full-text review, and data extraction." }, { "tracker_id": "hhs-2025-ee9dc76bbd", "fiscal_year": "FY2025", "use_case_name": "NewsScape", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Emergency Mgmt", "ai_type_as_reported": "Generative AI", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Development of early warning indicators. News articles can form an early warning indicator of public health events and other pieces of information which can be utilized across all major domains of public health. Because of the quantity of news articles, manual efforts are impossible to gather this information." }, { "tracker_id": "hhs-2025-90bb416c36", "fiscal_year": "FY2025", "use_case_name": "Portfolio Analytics", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "To automate the identification of themes within CDC-authored publications, providing richer information for science prioritization, evaluation, and communication." }, { "tracker_id": "hhs-2025-a670b47780", "fiscal_year": "FY2025", "use_case_name": "RAPID Analysis of Policy and Program Documents (RAPID)", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To streamline and automate the review and evaluation of policy and program documents, saving staff time, reducing the need for specialized training, providing consistent and complete answers, and enabling easy validation and collaboration." }, { "tracker_id": "hhs-2025-2df720e823", "fiscal_year": "FY2025", "use_case_name": "SewerScout: Automated on-site sewage facility detection from aerial imagery to identify failed systems", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Computer Vision", "public_health_functions": [ "surveillance", "environmental-occupational" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To automate the identification of onsite wastewater systems and detect failed systems using aerial imagery, enabling public health departments to efficiently locate and assess septic systems without resource-intensive field visits." }, { "tracker_id": "hhs-2025-3c8f7c9efd", "fiscal_year": "FY2025", "use_case_name": "Sidekick Comms bot Offering User-friendly Tips (SCOUT)", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To reduce the workload of health communicators by automating and simplifying the creation of web pages, social media posts, and other public-facing content, making information more accessible and understandable for the general public." }, { "tracker_id": "hhs-2025-3f0c8f6e57", "fiscal_year": "FY2025", "use_case_name": "Transcribing Cognitive Interviews with Whisper", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Reducing the time and effort required to transcribe cognitive interviews for federal health survey research, enabling faster and higher-quality analysis of qualitative data." }, { "tracker_id": "hhs-2025-6d37ec4cc1", "fiscal_year": "FY2025", "use_case_name": "Use of Natural Language Processing for Topic Modeling to Automate Review of Public Comments to Notice of Proposed Rulemaking", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Manual review of large volumes of public comments for Notices of Proposed Rulemaking is labor-intensive and time-consuming. The AI is intended to organize and cluster comments by theme, improving the efficiency and effectiveness of manual review and ensuring all topics are accurately reported." }, { "tracker_id": "hhs-2025-59b9362ac2", "fiscal_year": "FY2025", "use_case_name": "Short-term Forecasting of Severe Outcomes for Seasonal and Epidemic Pathogens", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Emergency Mgmt", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "surveillance", "forecasting" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Predict severe disease outcomes - such as emergency department visits or hospital admissions - over short time horizons (1-4 weeks) to improve situational awareness for planning and decision-making at the national, state, and local level. Traditional AI/ML models (e.g. time series models) are mainly used as baselines against which to test and improve more sophisticated modeling methods." }, { "tracker_id": "hhs-2025-bc668e44d4", "fiscal_year": "FY2025", "use_case_name": "CDC Chatbot - Enterprise Data Assistant", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This tool is a general purpose assistant for CDC staff to ask questions of large sets of documents. Staff may take hours to find the relevant information, and this tool enables fast access powered by Retrieval Augmented Generation (RAG)." }, { "tracker_id": "hhs-2025-4c549f47b5", "fiscal_year": "FY2025", "use_case_name": "Leveraging AI for Metadata Tagging for Enterprise Data Catalog of CDC", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Manual metadata tagging for datasets in the CDC Enterprise Data Catalog is inconsistent, incomplete, and time-consuming. The AI automates and standardizes metadata tagging, improving catalog usability and reducing manual effort." }, { "tracker_id": "hhs-2025-0c90df9d96", "fiscal_year": "FY2025", "use_case_name": "Malaria parasites DNA barcode geography classification", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To complement epidemiologic investigations of domestic malaria cases by determining the geographic origin of malaria parasite strains, helping to understand how the strain entered the US." }, { "tracker_id": "hhs-2025-7601ae1f84", "fiscal_year": "FY2025", "use_case_name": "The School Closure Awareness System", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Emergency Mgmt", "ai_type_as_reported": "NLP", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To efficiently and accurately identify and categorize unplanned school closures across the U.S. using publicly available social media data, replacing a costly and labor-intensive manual process." }, { "tracker_id": "hhs-2025-5710155a7c", "fiscal_year": "FY2025", "use_case_name": "Using Generative AI for Stance Analysis of Public Comments on CDC’s Proposed Rules", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Manual review of public comments for rulemaking is labor-intensive and time-consuming due to the volume and diversity of responses. The AI system automates stance analysis and topic modeling to improve efficiency, accuracy, and insight in the review process." }, { "tracker_id": "hhs-2025-b2fd7964d3", "fiscal_year": "FY2025", "use_case_name": "A reusable NLP pipeline for clinical narratives preprocessing and characterization", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-3e101c8003", "fiscal_year": "FY2025", "use_case_name": "Autocoding to Support Adverse Drug Event Surveillance", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "surveillance", "product-safety" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Manual coding of adverse drug event reports is time-consuming and slows down the production of prevalence estimates. The AI model will automate and speed up the coding process for surveillance epidemiologists." }, { "tracker_id": "hhs-2025-d286deb1a5", "fiscal_year": "FY2025", "use_case_name": "Automating LIMS Bioinformatics Workflow Configuration and Enhancing Lab Quality Management with AI", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Configuring and customizing bioinformatics workflows in Clarity LIMS is time-consuming and requires specialized expertise. The AI tool automates this process, enabling rapid deployment and lowering the barrier for laboratory staff. It also improves access to quality management and regulatory documentation." }, { "tracker_id": "hhs-2025-9280afd108", "fiscal_year": "FY2025", "use_case_name": "DGMH AI Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Responding to inquiries is time-consuming for staff, especially when information is available but hard to find on public CDC webpages. The AI chatbot drafts initial responses using content from CDC’s website, reducing turnaround time and freeing staff to focus on higher-priority tasks." }, { "tracker_id": "hhs-2025-68aa0fc88e", "fiscal_year": "FY2025", "use_case_name": "Distiller SR: AI to screen research articles for Community Guide reviews", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Screening research articles for systematic reviews is time-consuming. The AI tool will automate and speed up the process, supporting the Community Preventive Services Task Force in making timely recommendations." }, { "tracker_id": "hhs-2025-08f494205f", "fiscal_year": "FY2025", "use_case_name": "Evaluating Generative AI for polio containment.", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-bc37503a8f", "fiscal_year": "FY2025", "use_case_name": "Respiratory Virus Response (RVR) Data Analysis Concept", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [ "emergency" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Summarizing subject matter expert (SME) interpretations and knowledge during a public health response is time-consuming. The AI aims to improve efficiency in knowledge dissemination by generating key takeaways from SME-provided information." }, { "tracker_id": "hhs-2025-8f24600222", "fiscal_year": "FY2025", "use_case_name": "School LLM initial abstract review process", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Manual review and categorization of thousands of research abstracts related to school readiness science is time-consuming. The AI enables efficient extraction and categorization of themes, reducing human effort and time." }, { "tracker_id": "hhs-2025-c526b0ef29", "fiscal_year": "FY2025", "use_case_name": "CDC Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This tool is a general purpose assistant for CDC staff powered by Large Language Models. Staff can upload documents, summarize information, extract information, create content, develop software code, or general tasks to support operational efficiency." }, { "tracker_id": "hhs-2025-c1c6408001", "fiscal_year": "FY2025", "use_case_name": "EDAV Virtual Assistant - Eva ( Microbot Service) - Bot as a Service (BaaS)", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This tools serves as a helpdesk to support CDC staff in finding relevant information related to the Enterprise Data, Analytics, and Visualization platform available across existing documentation." }, { "tracker_id": "hhs-2025-279b0df5cf", "fiscal_year": "FY2025", "use_case_name": "EDAV Azure DataFactory - Pipeline failure Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "There are over 4,000 pipelines which provide logs of their status. Manual review is highly time-consuming and error prone due to the scale of logs." }, { "tracker_id": "hhs-2025-04a323afbb", "fiscal_year": "FY2025", "use_case_name": "1CDP (SEDRIC) AIP for Advanced Foodborne Outbreak Investigation (AI Summarization and Receipt Reading)", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Emergency Mgmt", "ai_type_as_reported": "NLP", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This AI solution impacts the process of investigating foodborne disease outbreaks. These foodborne outbreaks require cooperative efforts from CDC staff, FDA, USDA, and local agencies and the AI system is used through a centralized data platform System for Enteric Disease Response, Investigation, and Coordination (also known as SEDRIC). For more information on SEDRIC, please go to our website: https://www.cdc.gov/foodborne-outbreaks/php/foodsafety/tools/index.html" }, { "tracker_id": "hhs-2025-fd1b0e8fe3", "fiscal_year": "FY2025", "use_case_name": "Genetic distance computation method for comparing complex multi-locus parasite (Cyclospora) genotypes", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "surveillance", "laboratory" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Investigating the similarity of infections for epidemiologic investigations of cyclosporiasis outbreaks. The method enables clustering and comparison of complex genotypes, which are too large and complex for traditional methods, to identify related infections during outbreak tracking." }, { "tracker_id": "hhs-2025-0818e0af2b", "fiscal_year": "FY2025", "use_case_name": "MedCoder - Coding literal text cause of death information reported on death certificates to ICD-10", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Automating the coding of literal text causes of death from death certificates to ICD-10 codes, improving accuracy, efficiency, and timeliness of mortality data for public health surveillance." }, { "tracker_id": "hhs-2025-827f4013e0", "fiscal_year": "FY2025", "use_case_name": "NCIRD SmartFind ChatBots - Public and Internal", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Improving efficiency and effectiveness of internal partner mailbox email management and knowledge base maintenance for program staff, and previously, providing public-facing answers to FAQs." }, { "tracker_id": "hhs-2025-bdbd9409d4", "fiscal_year": "FY2025", "use_case_name": "NIOSH Industry and Occupation Computerized Coding System (NIOCCS)", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Replacing manual coding of industry and occupation text with automated coding to standardized codes, reducing cost and increasing speed, accuracy, and consistency for research and analysis." }, { "tracker_id": "hhs-2025-a4a53ba0b4", "fiscal_year": "FY2025", "use_case_name": "Nowcasting Injury Trends", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "surveillance", "forecasting" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Providing real-time estimates of injury and death trends to enhance situational awareness and expedite surveillance and research activities, especially when gold standard data are delayed." }, { "tracker_id": "hhs-2025-4c2ab0550e", "fiscal_year": "FY2025", "use_case_name": "Risk Assessment Module (RAM) for the National Diabetes Prevention Program (National DPP) Operations Center.", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To assist in determining if an organization participating in the National DPP is at risk of improperly starting, going inactive, or not achieving necessary goals for continued participation in CDC’s Diabetes Prevention Recognition Program." }, { "tracker_id": "hhs-2025-d2ef853272", "fiscal_year": "FY2025", "use_case_name": "Semi-Automated Nonresponse Detection for Surveys (SANDS)", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Manual review of open-ended survey responses is labor-intensive and cost-prohibitive at scale. SANDS automates the detection of nonresponses in survey data, reducing the burden on researchers and improving data quality." }, { "tracker_id": "hhs-2025-c4430f51b8", "fiscal_year": "FY2025", "use_case_name": "Sequential Coverage Algorithm (SCA) and partial Expectation-Maximization (EM) estimation in Record Linkage", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To improve the accuracy and efficiency of record linkage in CDC’s National Center for Health Statistics (NCHS) Data Linkage Program, particularly for large datasets, by automating the development and selection of blocking groups and reducing manual effort." }, { "tracker_id": "hhs-2025-5b930b8072", "fiscal_year": "FY2025", "use_case_name": "TowerScout: Automated cooling tower detection from aerial imagery for Legionnaires' Disease outbreak investigation", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Computer Vision", "public_health_functions": [ "surveillance", "environmental-occupational" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Rapid identification of cooling towers that could potentially be spreading Legionella bacteria during outbreaks, enabling faster and more efficient outbreak response." }, { "tracker_id": "hhs-2025-9f761d68fd", "fiscal_year": "FY2025", "use_case_name": "Assessing Large Language Models for Synthetic Survey Data Generation", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Survey data de-identification is crucial for the NCHS to maximize data utility while protecting privacy, but determining and applying modern best practices requires further research. NCHS conducts national surveys and releases microdata (data containing information about individuals) for public use. To protect survey participants’ confidentiality, statistical disclosure limitation techniques have been used to de-identify data, but these methods have drawbacks of losing statistical properties of the original data and thus limiting useful analyses. Additionally, these methods are not designed for very large data or text data. Use of synthetic data may offer another option. The goal of synthetic data is to preserve essential statistical features and variable relationships of the original data such that statistical inference based on the synthetic data is close to that of the original data. Large language models (LLMs) may be able to address limitations of statistical methods for synthetic data creation, especially for natural language data. We aim to advance knowledge of this application of LLMs to enable staff to select the optimal tools for synthetic data generation." }, { "tracker_id": "hhs-2025-593532fe8f", "fiscal_year": "FY2025", "use_case_name": "Development of in-silico genomic and patient datasets using generative ML algorithms", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-08a44b600b", "fiscal_year": "FY2025", "use_case_name": "GenAIMeta: Generative AI CDC Metadata Query Application", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Metadata plays a crucial role in enhancing public understanding and usage of CDC data. Usable metadata are essential not only for making data easy to find, understand, and use on data.cdc.gov but also for synchronizing with other federal catalogs. Metadata on data.cdc.gov spans 1,056 datasets with ~20 fields each, syncing nightly with federal catalogs. Manual validation, normalization and monitoring of this volume, and the inconsistent quality and completeness of those fields creates bottlenecks for data discovery, governance and downstream analytics. The aim is to leverage EDAV’s Azure OpenAI-powered models to automate metadata validation, standardization and monitoring at scale, replacing error-prone manual checks with real-time, AI-driven oversight." }, { "tracker_id": "hhs-2025-27ebb812a8", "fiscal_year": "FY2025", "use_case_name": "Detecting, evaluating, and redacting PII in NAMCS HC Component", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "How effective are open-source PII detection models in identifying and redacting PII? The National Center for Health Statistics’ (NCHS) Division of Health Care Statistics has collected millions of health records with laboratory results from encounters at health centers via the National Ambulatory Medical Care Survey (NAMCS), Health Center (HC) Component. Due to inadvertent errors during data entry and processing, some records contain identifiers (e.g. names, locations, etc.) in non-PII fields. Due to the PII, certain fields cannot be made available for restricted or public use, but reviewing millions of records for PII is not practical." }, { "tracker_id": "hhs-2025-bece2dc693", "fiscal_year": "FY2025", "use_case_name": "Retrieval Augmented Generation (RAG) with Q-Bank", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-66cd43f16c", "fiscal_year": "FY2025", "use_case_name": "Using generative AI to gain insight of older adult falls", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-5d3f0c088c", "fiscal_year": "FY2025", "use_case_name": "Nowcasting Burden and Infection Trends for Seasonal and Epidemic Pathogens", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Emergency Mgmt", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "surveillance", "forecasting" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Improve real-time estimates of disease burden and infection trends for better situational awareness for planning and decision-making at the national, state, and local level. Traditional AI/ML models (e.g. time series models) are mainly used as baselines against which to test and improve more sophisticated modeling methods." }, { "tracker_id": "hhs-2025-1930f129ef", "fiscal_year": "FY2025", "use_case_name": "Automating the distribution of CDC-State Department cables using AI models in the Global Health Center", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The output from the AI system is the automated and timely distribution of CDC-State Department cables. It delivers accurate, formatted messages directly to the intended recipients within seconds, ensuring fast and reliable communication without manual intervention." }, { "tracker_id": "hhs-2025-440498672b", "fiscal_year": "FY2025", "use_case_name": "DHDSP NOFO Technical Assistance Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The Division for Heart Disease and Stroke Prevention (DHDSP) experienced numerous challenges in coordinating with grantees/recipients to support questions related to their programs. These challenges often resulted in inefficiencies such as inconsistent communication, limited accessibility to data, potential inaccuracies in responses, and delayed responses. To address these issues, the TA Chatbot was developed to reduce the administrative burden on staff related to their assigned Technical Assistance (TA) case load." }, { "tracker_id": "hhs-2025-ae3fcdfa6d", "fiscal_year": "FY2025", "use_case_name": "Rapid Detection of Acute Releases of Toxic Substances (RaDARTS)", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Emergency Mgmt", "ai_type_as_reported": "NLP", "public_health_functions": [ "surveillance", "environmental-occupational" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Reporting on Acute Releases of Toxic substances involves review of new media sources and is a highly labor intensive process which may involve missed content. The goal is to rapidly ingest, categorize, summarize, and store data from news media sources to inform situational awareness and surveillance of Acute Releases of Toxic Substances." }, { "tracker_id": "hhs-2025-384608deb3", "fiscal_year": "FY2025", "use_case_name": "DLS AI Assistant tool", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Regulatory laboratory compliance has become increasingly complex, requiring scientists, lab personnel, and managers to understand laboratory quality regulations like the Clinical Laboratory Improvement Amendments (CLIA). The DLS AI Assistant is a tool designed to help scientists and lab workers by providing relevant guidance, such as the internal DLS Policy and Procedures Manual (DLS PPM) and external CLIA and International Organization for Standardization (ISO) regulations for laboratory quality. It offers personalized responses based on the user's level of expertise. In the final stage of the project, we plan to add a feature that will automatically evaluate Standard Operating Procedures (SOPs) to check if they comply with all relevant documents. However, the DLS AI Assistant is meant to assist and does not dictate compliance." }, { "tracker_id": "hhs-2025-1df196619b", "fiscal_year": "FY2025", "use_case_name": "PDF information extraction for 889 Forms", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Computer Vision", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Document processing and extraction of text from required 889 compliance forms (PDFs and/or image files) into a searchable cloud-based database. Eliminating manual data entry and providing a more transparent log of compliance related information in a repository for users to check, verify, and review." }, { "tracker_id": "hhs-2025-5cbd90c779", "fiscal_year": "FY2025", "use_case_name": "ATSDR Toxicological Assistant chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Generative AI", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The ATSDR Toxicological Assistant chatbot via the CDC Chatbot has access to query 180 comprehensive toxicological profiles (A-Z Index of Tox Profiles | Toxicological Profiles | ATSDR). The chatbot can assist users in accessing toxicological data, answering questions about specific chemicals, providing information on exposure pathways, and offering guidance on health assessments related to environmental contaminants. This tool can significantly reduce research time, ultimately enhancing efficiency in accessing critical toxicological information." }, { "tracker_id": "hhs-2025-5701af41da", "fiscal_year": "FY2025", "use_case_name": "Machine Learning with Premier Healthcare Data to inform predictive modeling of antibiotic use", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Surveillance data, including the Emerging Infections Program (EIP) and the National Healthcare Safety Network (NHSN) can be linked to and other inpatient data to better characterize risk factors and outcomes of important healthcare associated infections (HAI) and antimicrobial resistance (AR). However, discharge data often lacks detailed information about inpatient antibiotic use." }, { "tracker_id": "hhs-2025-5bcb73831a", "fiscal_year": "FY2025", "use_case_name": "Machine Learning Techniques for Early Detection and Situational Awareness of Rabies Outbreaks", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Rabies is enzootic in wildlife and modern-day surveillance techniques are too weak to fully capture the geographic extent of outbreaks nor early outbreak detection. Our ML algorithm uses public health surveillance data to \"fill in the gaps\" inherent in wildlife disease surveillance programs to accurately and rapidly detect outbreaks and deploy public health resources." }, { "tracker_id": "hhs-2025-50b3c6d4ad", "fiscal_year": "FY2025", "use_case_name": "AI-Powered web scanner for digital surveillance of rabies-related news articles", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Agentic AI", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "US travelers to international destinations are often exposed to rabies, and in some cases have died upon return to the US. Accurate and early identification of rabies outbreaks can help inform US travelers pre-travel healthcare and vaccine decisions. Unfortunately, surveillance for and transparency of rabies outbreaks in international settings is unreliable and rarely reported through official government channels." }, { "tracker_id": "hhs-2025-10ad2b4243", "fiscal_year": "FY2025", "use_case_name": "Pathogen strain characterization from mixed strain samples", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "surveillance", "laboratory" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "We use the DNA sequences at specific places in pathogen genomes to create a \"DNA fingerprint\" that allows us to link cases of diarrheal illness and identify potential foodborne outbreaks. When a single patient has more than one strain of the same pathogen (e.g. two pathogenic E. coli), the pieces of the DNA fingerprint get mixed together in the sample and make the data unusable for outbreak surveillance. Our ML-based method is intended to sort the pieces of the DNA fingerprints into separate strains and make this data usable for foodborne outbreak surveillance." }, { "tracker_id": "hhs-2025-aa61aaee06", "fiscal_year": "FY2025", "use_case_name": "AMD-Platform Data Harmonization", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Current Metadata may not match existing values, decreasing metadata quality. The harmonization of submitted deviant metadata to best match our current values to ensure accurate metadata is the goal, with an example being Mtb is converted to Mycobacterium tuberculosis." }, { "tracker_id": "hhs-2025-16ebdce128", "fiscal_year": "FY2025", "use_case_name": "Text embedding analysis tool", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Reviewing large sets of documents can take hours to identify similar clusters or analysis. The tool helps non-technical staff explore large, text-based datasets by generating clusters of text and identifying similar documents." }, { "tracker_id": "hhs-2025-d6d28f9a9c", "fiscal_year": "FY2025", "use_case_name": "Agentic RAG Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The creation of accurate, detailed, and factually-grounded draft responses to public inquiries takes a large amount of time and could benefit from assistance from LLMs. Because of how complex the responses to the inquiries can be, LLMs alone did not perform well enough to be useful in production, so we decided to build a more advanced chatbot that uses a small team of agents to refine the inquiries, decide what source data to use for grounding, and generate higher-quality draft responses to the inquiries that staff can use as a starting place for writing their replies." }, { "tracker_id": "hhs-2025-6706475a62", "fiscal_year": "FY2025", "use_case_name": "Internal Newsletter Formatter Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This project addresses the challenge of efficiently summarizing and formatting lengthy newsletter documents to provide leadership with clear, concise, and actionable information." }, { "tracker_id": "hhs-2025-e33d040320", "fiscal_year": "FY2025", "use_case_name": "Determining if multiple isolates share the same antimicrobial resistant plasmids using short read whole genome sequencing data", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Determining if multiple isolates share the same antimicrobial resistant plasmids using short read whole genome sequencing data." }, { "tracker_id": "hhs-2025-01c2189def", "fiscal_year": "FY2025", "use_case_name": "Analyzing multidrug-resistant organism response data", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Free text data are collected as part of internal tracking of Antimicrobial Resistance. These data are difficult to clean, categorize, and analyze." }, { "tracker_id": "hhs-2025-67f0b55171", "fiscal_year": "FY2025", "use_case_name": "Leveraging AI for the Creation of Synthetic Datasets", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "We aim to generate synthetic datasets that will support training, testing, and software development improving the overall development process" }, { "tracker_id": "hhs-2025-9d62f52c2b", "fiscal_year": "FY2025", "use_case_name": "DHP Data Repository and Dashboard Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "HP uses data to drive action for preventing and addressing HIV in the United States (U.S.). Historically, DHP’s data sources have not been easily accessible internally due to a siloed organizational structure. The DHP Data Repository and Dashboard aims to break down silos by co-locating and visualizing data from across the Division. Currently the repository and dashboard focus on the management and presentation of numeric data. By integrating advanced analytics and natural language processing capabilities into the existing repository we can increase the efficiency, accessibility, and usability of narrative information that is collected in DHP." }, { "tracker_id": "hhs-2025-aed6fc27c4", "fiscal_year": "FY2025", "use_case_name": "HIV Data Quality Score (DQS) Project", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Currently, 60 health departments and 150 community-based organizations submit National HIV Monitoring and Evaluation (NHM&E) program data using a standard online form. However, this data often contains errors that require significant manual cleanup by CDC's HIV data managers. To address this issue, our project aims to create a large language model (LLM)-based data quality score capable of detecting errors in datasets and measuring dataset cleanliness levels. LLMs can also be utilized to automatically fix some detected errors." }, { "tracker_id": "hhs-2025-6316a9bbe4", "fiscal_year": "FY2025", "use_case_name": "NCHHSTP Social Media Modernization Strategy: Thought Leadership and Social Listening", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Emergency Mgmt", "ai_type_as_reported": "NLP", "public_health_functions": [ "communication" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "To improve communication and ensure health messaging on social media platforms is responsive to the needs of the American people. AI-powered social listening tools are increasingly used in public health to analyze public conversations and audience sentiment in real time. We are using AI social listening tools to collect and analyze publicly available social media data. We then meet with a subject matter expert to discuss this information. Insights from the meeting with the SME and KPI data obtained natively on social platforms are used to draft, publish, analyze, and optimize social media content." }, { "tracker_id": "hhs-2025-e534e66bab", "fiscal_year": "FY2025", "use_case_name": "Lectora AI Toolkit and Microbuilder authoring tools", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-71c4ae2a28", "fiscal_year": "FY2025", "use_case_name": "Using LLM to optimize National Health Interview Survey (NHIS) case note information", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The National Health Interview Survey (NHIS) employs Census field representatives (FRs) who use open text fields, referred to as ‘case notes,’ to document their interactions with households during screening and interview processes. These case notes serve as a valuable resource, offering insights into the nature of these interactions and aiding in the identification of ‘base cases’—instances that may reveal significant data quality issues. Currently, the review of case notes is performed manually on a case-by-case basis, which limits opportunities for optimization. The objective of this initiative is to explore how large language models (LLMs) can enhance the efficiency and effectiveness of the case notes review process." }, { "tracker_id": "hhs-2025-90ffdcb9d4", "fiscal_year": "FY2025", "use_case_name": "Auto-Suggest Journal Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "It can be challenging and time-consuming for NCHS staff to research journals and gather the pertinent information to guide a decision of which journal to target for publication. Important information to consider when identifying a target journal includes the journal’s field or subject matter, word limits, formatting and submission guidelines, whether the journal is open access, the impact factor, and acceptance rate. Existing tools use keyword searching, term frequencies, and word similarity scoring to identify potential journal matches, but AI presents the potential for a more effective approach that can consider more factors." }, { "tracker_id": "hhs-2025-5f6cac0345", "fiscal_year": "FY2025", "use_case_name": "Use of National Language Processing/Machine Learning to Identify Personal Identifiers in Health Center EHR Medication Data", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The NLP/ML processes being used are attempting to identify personal identifiers within EHR data fields that capture medication information such as name of medication and dosage of medication." }, { "tracker_id": "hhs-2025-eb8db24ec5", "fiscal_year": "FY2025", "use_case_name": "AI-Assisted Extraction of Circumstance Information from National Violent Death Reporting System Narratives", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The National Violent Death Reporting System (NVDRS) compiles both quantitative and qualitative data on the circumstances surrounding violent deaths, including homicides and suicides, from three key data sources related to each death: the death certificate, the coroner/medical examiner report (including toxicology results), and the law enforcement report from the law enforcement agency that investigated the death. Data abstractors in state health departments across the U.S. abstract relevant information about each death from CME and LE reports, creating narratives that describe the most notable circumstances that contributed to the deaths captured in the system. Thus, much of the valuable contextual information about these deaths, such as details about chronic pain, interpersonal arguments, or other contributing factors, is embedded in free-text narratives and is not routinely abstracted into structured quantitative data fields. Manually extracting this information is labor-intensive, time-consuming, and subject to variability. The problem we are addressing with AI is the automated extraction of specific circumstance information from these unstructured narratives, enabling more comprehensive and systematic data analysis." }, { "tracker_id": "hhs-2025-307c5d37de", "fiscal_year": "FY2025", "use_case_name": "Leveraging GenAI for Efficient Review of CDC Programmatic Reports", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Manual qualitative review of text data within programmatic reports, such as Annual Progress Reports (APRs) submitted by Injury Control Research Centers (ICRCs) and Drug Free Communities (DFCs), is resource and time intensive. Across multiple use cases, generative AI (GenAI) and natural language processing (NLP) are being leveraged to automate the analysis of programmatic data. This approach streamlines the review and evaluation of various programmatic documents, improves efficiency, and supports the assessment of performance, progress, and challenges in funded activities." }, { "tracker_id": "hhs-2025-95e54d433a", "fiscal_year": "FY2025", "use_case_name": "Bridging the gap: Leveraging natural language processing to identify reasons for buprenorphine discontinuation in Electronic Health Records", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Life-saving treatment for opioid use disorder (OUD), such as the FDA-approved medication buprenorphine, remains underutilized. Buprenorphine has been shown to reduce illicit opioid use and risk of overdose mortality. Understanding treatment barriers can offer us opportunities for improved recovery. The PanTher Electronic Health Records (EHR) data from OptumLabs are a unique and important data asset, containing structured variables, such as diagnoses and procedures, laboratory measures, and medication records, as well as semi-structured data derived from clinical notes through natural language processing (NLP). The NLP-derived data contain helpful contextual information but have been difficult to use thus far." }, { "tracker_id": "hhs-2025-8c415bc590", "fiscal_year": "FY2025", "use_case_name": "Automating Influenza Vaccine Virus Data Processing", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Generative AI", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Information for candidate vaccine virus (CVV) data from public-facing websites is highly labor intensive to gather. Previously R scripts—text files with computer programming commands—automate some scraping processes but still rely on manual methods to extract data from PDFs." }, { "tracker_id": "hhs-2025-e812b2df14", "fiscal_year": "FY2025", "use_case_name": "Lineage Assignment by Extended Learning (LABEL)", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Identifying, classifying, and annotating influenza sequences." }, { "tracker_id": "hhs-2025-fb2d45d934", "fiscal_year": "FY2025", "use_case_name": "Enhancing influenza A risk assessment rubrics: leveraging predictive correlates and machine learning from in vivo experiments.", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The Pathogenesis Laboratory Team (Immunology and Pathogenesis Branch, Influenza Division, NCIRD) routinely performs influenza A virus (IAV) risk assessment studies in the ferret animal model, to assess IAV pathogenicity and transmissibility in a relevant small mammalian model. However, these studies are typically performed in isolation, with minimal efforts to comprehensively examine how each biological parameter obtained from the work correlates to disease severity and virus transmissibility and how these parameters can be used as a whole to improve risk assessment efforts. We have generated large sets of data collected from 25+ years of performing these in vivo studies, to identify predictive correlates associated with pathogenicity and transmissibility outcomes, and utilized machine learning approaches to better predict the potential public health risk posed by emerging influenza A viruses." }, { "tracker_id": "hhs-2025-41ff4adac4", "fiscal_year": "FY2025", "use_case_name": "RepoAnalysis", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "We are trying to generate a summary of code repositories that are insufficiently documented." }, { "tracker_id": "hhs-2025-31ea90fd45", "fiscal_year": "FY2025", "use_case_name": "Data Standardization with LLM", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The purpose of this project is to enhance data standardization efforts by leveraging large language models. to improve data cleaning and standardization processes, ultimately enhancing the overall efficiency and accuracy of data" }, { "tracker_id": "hhs-2025-399d0d0344", "fiscal_year": "FY2025", "use_case_name": "Using Databricks Genie for Routine Immunization Data Insights", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The volume of immunization data reported quarterly is approximately 5 billion records. Analyzing this data to gather high-level insights requires complex coding and manipulation. By using a Large Language Model (LLM) based approach, the solution offers a plain language query capability that generates code to provide high-level insights without the need to move or create specific views for program needs. This approach is cost-effective, timely, and provides insights that can be used to improve data quality and inform data management planning by programs." }, { "tracker_id": "hhs-2025-a129133b5d", "fiscal_year": "FY2025", "use_case_name": "Vaccine Tracking System (VTrckS) Conversational AI Pilot", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The VTrckS Conversational AI module will provide a user-friendly prompt driven approach to gaining insights into the VTrckS datasets. Current approach involves utilizing the SAP HANA’s reporting modules and custom dashboards. The conversational AI approach will allow for a more human friendly metto quick insights without having to learn about the underlying data models." }, { "tracker_id": "hhs-2025-7da8d3383e", "fiscal_year": "FY2025", "use_case_name": "Beryllium exposure reconstruction", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Using machine learning to extract information about job title and task and assign relevant exposure codes." }, { "tracker_id": "hhs-2025-3d14ef4c0a", "fiscal_year": "FY2025", "use_case_name": "Site Audit AI Support (SAAIS) App", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety", "environmental-occupational" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "NIOSH conducts numerous post-market activities to ensure that respirator configurations approved by NIOSH remain protective. Among these activities are audits of the manufacturing sites where the approved products are produced. These audits involved detailed evaluations of sites against quality assurance plans approved by NIOSH. Site are audited every few years and, thus, previous reports of nonconformances may be of particular interest to auditors. SAAIS is a prototype application being developed as an AI use case to inform: 1. CDC Office of the Chief Information Officer (OCIO)’s implementation strategy for cloud-based enterprise services standalone AI tools and AI-enhanced systems 2. The specific enterprise approach, AI tools, and training techniques to be used by Respirator Approval System (RAS) developers when AI enhancements are eventually added following the completion of the base system within the Power Platform enterprise system." }, { "tracker_id": "hhs-2025-46b8bcd24f", "fiscal_year": "FY2025", "use_case_name": "Respirator Selection Logic (RSL) Copilot", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "NLP", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Workers rely on NIOSH Approved respirators to protect them from inhaling high-consequence particulate, gas, and vapor hazards. Some examples of these respiratory hazards include: • Wildfire, structural, or surgical smoke • Mold during post-flood remediation efforts • Infectious diseases such as tuberculosis • Chemicals used to clean or disinfect • Particles liberated when cutting rock in industries such as construction and mining Selecting the correct respirator to protect workers requires knowledge of the hazard or hazards present, the job task, and the environment. NIOSH’s Respirator Selection Logic (RSL) is a state-of-the-art tool designed to guide the selection of appropriate respiratory protection devices based on specific workplace hazards and conditions. The RSL requires users to enter detailed, task- and environment-specific information at multiple decision points to execute its logic correctly." }, { "tracker_id": "hhs-2025-3b14511977", "fiscal_year": "FY2025", "use_case_name": "PPE Concerns Copilot", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "NLP", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The PPE Concerns Mailbox received inquiries that NIOSH/NPPTL staff must review and respond to. To date, over 10,000 questions have been received with answers provided. Responding to an inquiry requires a multi-step process that staff must complete manually, including • Reading the inquiry • Logging the inquiry and information about the inquiry and inquirer in an Excel spreadsheet • Searching through the spreadsheet of past questions • Reviewing prior responses for relevance • Drafting a reply based on similar previous responses or researching and composing a new answer from scratch if no match is found • Sending response to additional staff if further subject matter expert review is needed • Obtaining Executive Leadership review and approval, if needed • Updating the spreadsheet with the finalized response, reply date, and staff who assisted with the response This process can be time-consuming, repetitive, and inconsistent, especially when multiple team members are handling and categorizing inquiries or when there are high volumes." }, { "tracker_id": "hhs-2025-a68a443729", "fiscal_year": "FY2025", "use_case_name": "Assessing public comment responses to draft NIOSH wildland fire smoke document", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Efficiently compile and synthesize the public comments received for a draft NIOSH document." }, { "tracker_id": "hhs-2025-a1440c267c", "fiscal_year": "FY2025", "use_case_name": "Mining.AI", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "NLP", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Mining.AI is an innovative initiative led by a small team at NIOSH tasked to develop a domain-specific generative LLM AI platform focused on improving health and safety in the mining industry. The project consolidates NIOSH's latest peer-reviewed research and safety knowledge to support decision-making, hazard prevention, and incident response." }, { "tracker_id": "hhs-2025-8306c82c91", "fiscal_year": "FY2025", "use_case_name": "CSB MCP AI", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Reduce required manhours and improve accuracy of reports on CSB infrastructure and by assisting infrastructure administrators with tasks and duties." }, { "tracker_id": "hhs-2025-e5f5601ccf", "fiscal_year": "FY2025", "use_case_name": "Support for MCP and AI APIs in Digital Gateway", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Digital Gateway (CDC's API Platform) can support MCP and AI APIs. Given MCPs are a new addition to the ecosystem, the goal is to explore what it takes to implement MCPs internally." }, { "tracker_id": "hhs-2025-78b5db8c20", "fiscal_year": "FY2025", "use_case_name": "Delegations Repository Assistant", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The Delegations Library, which houses critical guidance on authorities and approvals, has been selected as the pilot use case due to its centralized structure and high relevance. Staff frequently report difficulty locating the right documents, resulting in delays and inefficiencies. This project will use CDC’s internal EDAV platform to develop a chatbot assistant that helps users retrieve existing content more easily. The proof of concept will test technical feasibility, user experience, and potential for broader application to support CDC’s operational mission." }, { "tracker_id": "hhs-2025-8b7ace8ba4", "fiscal_year": "FY2025", "use_case_name": "OFR Robotics and Process Automation (ORPA)", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Procurement and Finance Mgmt", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Reduction in human workload and increase in quality through automation of repetitive tasks." }, { "tracker_id": "hhs-2025-a519c8a5c2", "fiscal_year": "FY2025", "use_case_name": "FERRET", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "High throughput processing of unstructured data necessary for mandated reporting." }, { "tracker_id": "hhs-2025-6e54207e2d", "fiscal_year": "FY2025", "use_case_name": "Deep Research for Public Health", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Public health agencies like the CDC face challenges in efficiently processing large volumes of complex information, conducting evidence-based research, and producing timely, high-quality analyses and reports to inform decision-making. Traditional workflows for tasks such as literature review, data analysis, policy evaluation, and communications are often time-consuming and resource-intensive. The problem addressed by integrating agentic AI models, such as OpenAI’s Deep Research, is to enhance the efficiency, productivity, and rigor of these core public health functions by automating information retrieval, synthesis, and analysis, thereby enabling faster, more informed, and scalable decision-making while maintaining quality and transparency." }, { "tracker_id": "hhs-2025-f2463245bc", "fiscal_year": "FY2025", "use_case_name": "Federal Select Agents Program (FSAP) Customer Agent", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "FSAP users need support on the use of the FSAP system. Completing forms, managing entities, agents, and toxins. Updating permits, correcting data, conducting inspections, and related activities. The FSAP Customer Agent will provide answers to questions as users navigate the FSAP process." }, { "tracker_id": "hhs-2025-a82b9e82b5", "fiscal_year": "FY2025", "use_case_name": "Publication Portfolio Analytics", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The Data Strategy and Analytics Team (DSAT) within OS employs natural language processing (NLP) topic modeling techniques to help programs identify common themes within their publication data. By combining these efforts with bibliometric analysis, we standardize the reporting of media attention, as well as policy and academic citations, by theme and/or CDC organization. Automating these efforts helps the CDC library to optimize their allocation of resources and avoid duplication of effort." }, { "tracker_id": "hhs-2025-1ff03ce380", "fiscal_year": "FY2025", "use_case_name": "CDC Vault and Stacks Metadata Extraction", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "We are attempting to speed up the process of generating a digital metadata record for objects that will be curated and stored into either CDC Public Access Platform (Stacks) or CDC Vault. These two systems are built using the same software stack but one is for public data and the other is for non-public data. To create a metadata record solely with a human, the process takes about an hour per document. We are looking to improve the process to use AI to prepare the metadata record and reduce the human time to under 5 minutes. A secondary objective is to have a non-human process for the non-public data that will go into CDC Vault." }, { "tracker_id": "hhs-2025-657148a0a7", "fiscal_year": "FY2025", "use_case_name": "AIP Assist", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "AIP assist supports user engagement with the Palantir Platform. It is an LLM-powered support tool that is able to answer inquiries about the platform and can also guide users in developing their own applications by supporting users to write their own code or pointing the user to the right tool for the task. It supports assistance for platform navigation, coding tasks, documentation support, and problem solving for users." }, { "tracker_id": "hhs-2025-9a849ab586", "fiscal_year": "FY2025", "use_case_name": "Center for Forecast and Analytics (CFA) Model Studio", "previous_name_reported": "", "bureau_as_reported": "HHS/CDC", "component": "CDC", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Other", "public_health_functions": [ "forecasting" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This tool is intended to provide streamlined infrastructure around allowing users to bring their own models or discover existing ones and evaluate them in a streamlined way. For example, users can develop their models in their local machine, containerize them and deploy them using CFA Model Studio in the platform for further experimentation, parameter exploration and registry." }, { "tracker_id": "hhs-2025-9ab3c7680c", "fiscal_year": "FY2025", "use_case_name": "Enhanced Direct Enrollment Outlier Detection", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "program-integrity" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "(Marketplace) Enhanced Direct Enrollment (EDE) allows consumers to apply for and enroll in an exchange plan directly through an approved partner's UI, without being redirected through the Healthcare.gov application. These partner systems directly interface with the APIs developed by the FFE. As EDE partners gain more control over their application process, the FFE must ensure program integrity." }, { "tracker_id": "hhs-2025-c2f89e9ca6", "fiscal_year": "FY2025", "use_case_name": "IT System Utilization Optimization", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "(Marketplace) The FFE application usage patterns vary and are dependent on differing environment usage periods. CMS resources are currently manually scaled, not allowing immediate actions in correlation with usage changes." }, { "tracker_id": "hhs-2025-d98e922d25", "fiscal_year": "FY2025", "use_case_name": "Risk Adjustment Outlier Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "program-integrity" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "The risk adjustment (RA) program spreads the financial risk borne by Issuers due to offering a variety of plans meeting the need of the diverse population. RA payments are distributed based on population risk levels. CCIIO uses a distributed data solution (EDGE servers) to calculate plan average actuarial risk and associated RA transfers and must avoid potential program integrity risks to annual calculations." }, { "tracker_id": "hhs-2025-5b1277ec40", "fiscal_year": "FY2025", "use_case_name": "Agent/Broker Fraud Analysis (ABCQI)", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "program-integrity" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "(Marketplace) Agents and brokers support the consumer enrollment and eligibility process. Because of this, they have learned the intricate details of the Federally-facilitated Exchange (FFE) for accessing applications, submitting eligibility determinations, and adding enrollments to their line of business, opening up the possibility of fraud." }, { "tracker_id": "hhs-2025-7aa7023be6", "fiscal_year": "FY2025", "use_case_name": "Help Desk Responses", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "(Marketplace) The Division of Issuer Management and Operations has talked with their contractor, LMI, about using an AI tool for the Help Desk contract." }, { "tracker_id": "hhs-2025-a8eb827f2d", "fiscal_year": "FY2025", "use_case_name": "Chatbot within Hub", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "(Other) Currently the following help support activities are handle by a human: - Personalized FAQ?s - Clarify questions with schema and onboarding, etc. - Handle \"Where is 'My file' or 'My Request'?\" inquiries - Provide a \"Talk to Agent\" feature - Schedule a testing window - Provide data summarization and reporting for internal stakeholders - Report operational health of the system - Include training materials, Q/A about the system" }, { "tracker_id": "hhs-2025-e4488fea2c", "fiscal_year": "FY2025", "use_case_name": "Independent Dispute Resolution (IDR) Eligibility Rules Engine", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "The current Independent Dispute Resolution (IDR) Technical Assistance (TA) process is very manual and time intensive which limits throughput. The rules engine should significantly expedite processing and expand capacity. Automating more of the process may also increase consistency across recommendations and result in a more predictable timeframe for the workflow by better positioning disputes for analyst review." }, { "tracker_id": "hhs-2025-48de7eea29", "fiscal_year": "FY2025", "use_case_name": "Review Regulatory Comments", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "(Marketplace) Public comment analysis in the Letter to Issuers (LTI) and Notice of Benefits and Payment Parameters (NBPP) to streamline review of comments." }, { "tracker_id": "hhs-2025-ed28e96032", "fiscal_year": "FY2025", "use_case_name": "Improved Data Quality Checks", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "(Marketplace) Improve consumer experience by providing an asynchronous (not at the same time) solution to detect and provide near real time feedback via outreach to the consumer, shortening the overall return cycle time without requiring UI changes." }, { "tracker_id": "hhs-2025-520679806b", "fiscal_year": "FY2025", "use_case_name": "iVeri-Fi (Test)", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "High-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "In October 2024, Serco will begin utilizing iVeri-Fi, (a decision service platform) to perform automated processing of remote identity proofing (RIDP) verification tasks. These tools are already in our Eligibility Workers Support System (EWSS) stack and have an existing Authority to Operate (ATO). We are not introducing any new technologies - we are just changing how and where the work is done through automation (before Task Inconsistency Processing System (TIPS) not integrated with TIPS)." }, { "tracker_id": "hhs-2025-fb846bb14e", "fiscal_year": "FY2025", "use_case_name": "Plan Justification Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "(Marketplace) Identify trends/patterns within plan certification justification templates, containing free-form text data through use of natural language processing (NLP) and classification techniques." }, { "tracker_id": "hhs-2025-75bddb3f42", "fiscal_year": "FY2025", "use_case_name": "TIC URL Automation", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "(Marketplace) Issuers applying for QHP (Qualified Health Plan) certification, including issuers offering off-Exchange SADPs (stand alone dental plans), must submit a Transparency in Coverage URL in MPMS that leads to a page on the issuer's website where required information is posted. The current review process requires reviewers to manually read and verify the language presented on each TIC URL for compliance, which is both time-consuming and labor-intensive." }, { "tracker_id": "hhs-2025-8d7e3d0183", "fiscal_year": "FY2025", "use_case_name": "Rx Data Integrity Review", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The current process for conducting the Rx Data Integrity review is time intensive and requires reviewers to manually review online formularies in a condensed timeline. The purpose of this proposal is to develop a new process, which will automate as much of the review as possible, using existing language models as well as large language models (LLM) to introduce efficiencies and increase data accuracy." }, { "tracker_id": "hhs-2025-e558286581", "fiscal_year": "FY2025", "use_case_name": "RA and RADV Predictive Modeling and Methodology Evaluation", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "(Marketplace) Risk adjustment data validation (RADV) is the audit of HHS-operated Risk Adjustment (RA). KPMG, the main RADV contractor, seeks to conduct predictive modeling and simulations of proposed policy changes. KPMG also validates the RA models (ML-based) and evaluates the RADV methodology." }, { "tracker_id": "hhs-2025-e2e829ab35", "fiscal_year": "FY2025", "use_case_name": "RADV Medical Record Review", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "(Marketplace) RADV entails the review of enrollees' medical records to determine if diagnoses submitted to CCIIO for purposes of calculating RA transfers actually exist. This medical record review is time- and resource-intensive for KPMG (and therefore CCIIO)." }, { "tracker_id": "hhs-2025-99cafa1ad2", "fiscal_year": "FY2025", "use_case_name": "Issuer Expansion/Market Entry Prediction", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "CCIIO FO conducts targeted outreach to issuers likely to enter the markets or expand to other states" }, { "tracker_id": "hhs-2025-8f926e9e59", "fiscal_year": "FY2025", "use_case_name": "SBC Content Review", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "(Marketplace) The SBC Content Review identifies cost-sharing discrepancies between the Plans and Benefits Template and SBC Form. If there are discrepancies in the numerical values input in the two data sources, reviewers must manually review the \"Limitations, Exceptions and Other Information\" in the SBC Form to assess if a true discrepancy exists. The purpose of the proposal is to integrate large language models (LLM) to programmatically review qualitative data in the SBC Form." }, { "tracker_id": "hhs-2025-cc779a504e", "fiscal_year": "FY2025", "use_case_name": "MFT UI Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Hub/MFT team manually supports operations related to onboarding, Q/A, file tracking and technical inquiries. ? Monthly Average of inquiries: ? ~800 Email inquires ? ~40 Jira/SNOW Tickets? The Communication is manual via emails, Zoom Meetings, and Slack. This Current process is time-consuming, adds and degrades stakeholder experience.? ? Typical operational hours are Monday through Friday, 8 a.m. to 6 p.m. ET (this doesn’t serve Mountain Time, West Coast, Hawaii, and Alaska), followed by XOC escalations. This means non-prod inquiries will be responded to the following business day. ?? We have limited, Quality of Service (QoS) metrics to accurately assess consumer satisfaction at present time." }, { "tracker_id": "hhs-2025-c2d066ae51", "fiscal_year": "FY2025", "use_case_name": "MLMS Upgrade MILA from CARLA to Druid", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "New chatbot proposed by CMS MLMS team will be integrated into the existing MLMS application. It aims to improve user experience, designed to maintain or boost deflection rates, and will be hosted within the CMS environment for smooth integration." }, { "tracker_id": "hhs-2025-143ad7af26", "fiscal_year": "FY2025", "use_case_name": "Interoperability URL Review Automation", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCIIO", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Enable the team to save time and man hours, cost savings" }, { "tracker_id": "hhs-2025-c5497b4966", "fiscal_year": "FY2025", "use_case_name": "Feedback Analysis Solution (FAS)", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCSQ", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Speeds up the manual and time-consuming process of analyzing public comments on frequently posted regulations.gov and FDMS dockets by employing advanced natural language processing and machine learning technologies." }, { "tracker_id": "hhs-2025-8bb00c177f", "fiscal_year": "FY2025", "use_case_name": "CCSQ ServiceNow AI Search", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCSQ", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "The AI Search index stores data from ServiceNow AI Platform® records or external sources and makes that data available for users to search in multiple applications. Search query features use ServiceNow AI Platform technologies to improve the search user experience." }, { "tracker_id": "hhs-2025-3185b59eb9", "fiscal_year": "FY2025", "use_case_name": "Resource Library Assistant", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCSQ", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Users being able to find information related to program participation." }, { "tracker_id": "hhs-2025-9b1ad22770", "fiscal_year": "FY2025", "use_case_name": "QPP Admin Bot", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCSQ", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "This AI is to help with Code complexity." }, { "tracker_id": "hhs-2025-4481d13141", "fiscal_year": "FY2025", "use_case_name": "CCSQ Now Assist for CSM", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCSQ", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "CCSQ Now Assist for CSM (Customer Service Management) is a product inside ServiceNow (SaaS). It integrates Gen AI with CSM, and uses Now LLM (ServiceNow native Large Language Model) to generate contents based on machine learning (ML). Now Assist for CSM helps agents to improve productivity and efficiency and deliver better services. Improve agents' responsiveness and productivity * Quickly get familiar with a case/chat by getting case/chat summarization * Quickly resolve the case by using auto-generated resolution notes." }, { "tracker_id": "hhs-2025-df191b8386", "fiscal_year": "FY2025", "use_case_name": "Citation Analysis and Survey Assistant (CASA - Nursing Home Survey CMS 2567)", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCSQ", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "CASA enhances the efficiency and effectiveness of monitoring and reviewing nursing home surveys across the US. It enhances how the Quality, Safety, and Oversight Group (QSOG) and Survey Operations Group (SOG) assess how State Survey Agencies (SSAs) are citing nursing home deficiencies, reported on CMS Form 2567, by employing advanced natural language processing and machine learning technologies." }, { "tracker_id": "hhs-2025-95abeaa228", "fiscal_year": "FY2025", "use_case_name": "CCSQ Now Assist for Creator", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CCSQ", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Improve efficiencies regarding development withing the CCSQ ServiceNow program." }, { "tracker_id": "hhs-2025-884b7dc1a6", "fiscal_year": "FY2025", "use_case_name": "AI-assisted comment triaging tool", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CM", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To reduce labor hours of manual comment review we use AI to assist in comment review and triage, and in identifying form letters." }, { "tracker_id": "hhs-2025-7d1d51f3fd", "fiscal_year": "FY2025", "use_case_name": "Docketscope Public Comment Processing", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CM", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Docketscope is the platform used to triage public comments submitted on the OPPS/ASC and ESRD PPS proposed rules." }, { "tracker_id": "hhs-2025-70ed198042", "fiscal_year": "FY2025", "use_case_name": "Health Plan Management System - Complaint Tracking Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CM", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Support the analysis of MA and Part D beneficiary complaints data." }, { "tracker_id": "hhs-2025-e6eef77e98", "fiscal_year": "FY2025", "use_case_name": "Performance Metrics Database and Analytics (PMDA)", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CMCS", "component": "CMS", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-3406b41f60", "fiscal_year": "FY2025", "use_case_name": "T-MSIS Prima", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/CMCS", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "T-MSIS program objectives, facilitates data driven decisions, and IT acceleration" }, { "tracker_id": "hhs-2025-3ef90ffcce", "fiscal_year": "FY2025", "use_case_name": "EPRO HTA Reporting", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/EPRO", "component": "CMS", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-b53b45119c", "fiscal_year": "FY2025", "use_case_name": "CMS Labor Analysis Wizard (CLAW)", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OAGM", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Procurement and Finance Mgmt", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "CLAW provides semi-automated analysis and insights into contractor-proposed labor for evaluation and negotiation purposes, enabling OAGM to make more informed procurement decisions that optimize contract terms and enhance the efficiency of the federal acquisition process." }, { "tracker_id": "hhs-2025-e0bf817e7b", "fiscal_year": "FY2025", "use_case_name": "AI Code Assistance – WETG GitHub CoPilot Proof-of-Concept", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OC", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Coding assistance" }, { "tracker_id": "hhs-2025-8e9c82f776", "fiscal_year": "FY2025", "use_case_name": "AI Search in Google Vertex (discovery)", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OC", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Improved search" }, { "tracker_id": "hhs-2025-1f2b7972d4", "fiscal_year": "FY2025", "use_case_name": "AI in Slack", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OC", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Improved Slack" }, { "tracker_id": "hhs-2025-1f6722a20c", "fiscal_year": "FY2025", "use_case_name": "AI in Figma Make", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OC", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Rapid prototyping" }, { "tracker_id": "hhs-2025-ba34610173", "fiscal_year": "FY2025", "use_case_name": "AI Language Translation Support in Smartling", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OC", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Improved translation" }, { "tracker_id": "hhs-2025-6dfbb496a1", "fiscal_year": "FY2025", "use_case_name": "AI Web Content Search Engine Optimization (SEO) in Drupal", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OC", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Search engine optimization" }, { "tracker_id": "hhs-2025-dabc6e2dd1", "fiscal_year": "FY2025", "use_case_name": "Medicare AI Customer Insights", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OC", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Improved customer insights" }, { "tracker_id": "hhs-2025-a56bbc8258", "fiscal_year": "FY2025", "use_case_name": "Medicare AI Drug Search", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OC", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Benefits Processing", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Improved customer experience" }, { "tracker_id": "hhs-2025-b8372ba007", "fiscal_year": "FY2025", "use_case_name": "Marketplace Qualified Health Plan Benefit AI Assistant (discovery)", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OC", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Benefits Processing", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Improved customer experience" }, { "tracker_id": "hhs-2025-f097f0d762", "fiscal_year": "FY2025", "use_case_name": "AI Automation within the WETG Web Help Service Desk (discovery)", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OC", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Improved help desk service" }, { "tracker_id": "hhs-2025-5a18fb0c39", "fiscal_year": "FY2025", "use_case_name": "Artificial Intelligence Use Case Pilot in Medical Review on Medicare Fee-For-Service (FFS) Improper Payment Measurement (Comprehensive Error Rate Testing Program) Data", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OFM", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [ "program-integrity" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "OFM is hoping to leverage cutting-edge AI technology to potentially introduce new efficiencies and accuracy in the Comprehensive Error Rate Testing (CERT) medical review process by moving away from manual medical review which is costly, sometimes inaccurate, and inefficient." }, { "tracker_id": "hhs-2025-38af89e478", "fiscal_year": "FY2025", "use_case_name": "Knowledge Management Solution", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OFM", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This AI-driven system addresses a critical challenge in government operations - preserving institutional knowledge." }, { "tracker_id": "hhs-2025-ea067ea3ee", "fiscal_year": "FY2025", "use_case_name": "ASSIST Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OFM", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This tool focuses on strategic alignment and mission effectiveness across the team." }, { "tracker_id": "hhs-2025-0b6826aae3", "fiscal_year": "FY2025", "use_case_name": "Executive Order Gap Analysis Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OFM", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "A specialized compliance and analysis solution that analyzes Executive Orders and program impact." }, { "tracker_id": "hhs-2025-19b81c86e8", "fiscal_year": "FY2025", "use_case_name": "Contract Invoice Analyzer Assistant", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OFM", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Procurement and Finance Mgmt", "ai_type_as_reported": "Generative AI", "public_health_functions": [ "program-integrity" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Uses AI and automation to provide a more comprehensive review of contract invoices, mitigating waste, fraud, and abuse." }, { "tracker_id": "hhs-2025-844b29e7a5", "fiscal_year": "FY2025", "use_case_name": "AI-Powered Meeting Notes for MAG Hearings and AIRC Sessions", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OHI", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI is intended to address process redundancy by streamlining repetitive tasks, reducing manual effort, and minimizing duplication across workflows. This allows teams to operate more efficiently and focus on higher-value activities." }, { "tracker_id": "hhs-2025-7e1f627e3e", "fiscal_year": "FY2025", "use_case_name": "AI-Generated Pre-Briefs for MAG Hearings and AIRC Sessions", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OHI", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Utilize an AI system to compile structured pre-briefs for MAG Hearings and AIRC sessions." }, { "tracker_id": "hhs-2025-cd167d53d3", "fiscal_year": "FY2025", "use_case_name": "Knowledge Management Solution for Appeal Case Workers", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OHI", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Utilize an AI solution to create a centralized, AI-enhanced Knowledge Management system to support case workers by providing quick access to relevant SOPs, policy guidance, workflows, forms, and call scripts" }, { "tracker_id": "hhs-2025-a5e71386f0", "fiscal_year": "FY2025", "use_case_name": "AI Workspace", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Hosted development env with access to AI tooling, including LLMs for use in early stage exploration." }, { "tracker_id": "hhs-2025-a22e27e984", "fiscal_year": "FY2025", "use_case_name": "CMS Chat", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Workforce productivity and operational efficiency" }, { "tracker_id": "hhs-2025-7eae4515ab", "fiscal_year": "FY2025", "use_case_name": "Content Analysis POC", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Content analysis POC for the automated analysis of qualitative data including comments, complaints, stakeholder interviews, surveys, etc." }, { "tracker_id": "hhs-2025-43e8af5043", "fiscal_year": "FY2025", "use_case_name": "Enterprise Architecture LLM for CMS Regulatory Content", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Enhance the Enterprise Architecture (EA) Knowledgebase by creating a centralized, searchable repository of CMS regulatory knowledge, and make complex regulatory information available within the EA environment." }, { "tracker_id": "hhs-2025-8158b9ec36", "fiscal_year": "FY2025", "use_case_name": "Text Network Analysis POC", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-404c0da473", "fiscal_year": "FY2025", "use_case_name": "RFI Comment Analysis POC", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Addresses the challenge of efficiently analyzing and extracting insights from large volumes of comments in response to an RFI." }, { "tracker_id": "hhs-2025-e7f1ffa674", "fiscal_year": "FY2025", "use_case_name": "AI Agent Orchestrator POC", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI Agent Orchestrator solves the problem that current chatbots are limited to question-and-answer interactions and cannot orchestrate complex, multi-step analytical workflows that require calling external data science notebooks and tools in the correct sequence to solve sophisticated problems." }, { "tracker_id": "hhs-2025-fbfa95e193", "fiscal_year": "FY2025", "use_case_name": "MCP Server Registry and Integration Platform", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The MCP Server Registry solves the problem that CMS teams currently lack a centralized, standardized platform to develop, share, and integrate specialized AI tools and capabilities across the organization" }, { "tracker_id": "hhs-2025-9d47a6c9a6", "fiscal_year": "FY2025", "use_case_name": "Agentic Web Search", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI-powered web search agent solves the problem that CMS's custom chatbot, CMS Chat currently lacks internet access and is limited to training data with a cutoff date, preventing staff from accessing real-time information" }, { "tracker_id": "hhs-2025-db09df94fe", "fiscal_year": "FY2025", "use_case_name": "Deep Research Multi-Agent System", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The Deep Research multi-agent system solves the problem that CMS employees currently cannot conduct research directly within CMS Chat." }, { "tracker_id": "hhs-2025-5e767eaecf", "fiscal_year": "FY2025", "use_case_name": "508 Compliance Review MCP Server", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The 508 Compliance Review MCP server solves the problem that CMS staff currently must manually review documents for Section 508 accessibility compliance, which requires specialized knowledge of accessibility standards, is time-consuming, and may result in inconsistent evaluations" }, { "tracker_id": "hhs-2025-21c5668075", "fiscal_year": "FY2025", "use_case_name": "PubMed Literature Review MCP Server", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The PubMed Literature Review MCP server solves the problem that CMS teams need to carry out various types of literature reviews, commonly using PubMed data, but lack an internal tool for doing this at greater scale than manual effort allows" }, { "tracker_id": "hhs-2025-10a025fc5f", "fiscal_year": "FY2025", "use_case_name": "SAM.gov RFI Comment Analysis POC", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The SAM.gov RFI Comment Analysis POC solves the problem that CMS procurement staff currently must manually review and score vendor responses to Requests for Information (RFI) posted on SAM.gov, which is time-intensive, especially when there are many questions and many vendors responding." }, { "tracker_id": "hhs-2025-e59518dc89", "fiscal_year": "FY2025", "use_case_name": "C3PO: CMS Comprehensive Cybersecurity and Privacy Optimization", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Reduce the burden of reviewing lengthy OMB, NIST and HHS policy documents, quicker review of SORN and PIAs" }, { "tracker_id": "hhs-2025-0a69a9dcd8", "fiscal_year": "FY2025", "use_case_name": "Integrated Data Repository (IDR) Customer Analytic Environment (CAE)", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Provides an AI/ML environment connected to the IDR that enables CAE customers to focus on their AI use cases and business outcomes, without the burden of setting up or maintaining their own IT infrastructure or AI/ML labs or environments." }, { "tracker_id": "hhs-2025-20fdf9f037", "fiscal_year": "FY2025", "use_case_name": "IDR Support Bot", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The IDR Support Bot is an AI-powered chatbot that makes it easier for CMS users to get the IDR information they need, when they need it. It answers questions about IDR terminology, tools and services, onboarding steps, available data, training content, key contacts, and more—without the hassle of submitting a support request or digging through pages of documentation." }, { "tracker_id": "hhs-2025-5b74f5912b", "fiscal_year": "FY2025", "use_case_name": "Eligibility & Enrollment Medicare Online (ELMO) Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Assist Medicare caseworkers in navigating a fairly complex application" }, { "tracker_id": "hhs-2025-4e4204b0ba", "fiscal_year": "FY2025", "use_case_name": "Production Operations Anomaly Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Identify potential production issues proactively and refer for human analysis" }, { "tracker_id": "hhs-2025-76d1c2cc63", "fiscal_year": "FY2025", "use_case_name": "Case Management Tool Case Creation & Automation", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OIT", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Automate and optimize manual case creation for incoming documents/requests and simplify manual tasks" }, { "tracker_id": "hhs-2025-89a2dbf593", "fiscal_year": "FY2025", "use_case_name": "Complaint Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OPOLE", "component": "CMS", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Conduct high volume case analysis to identify root causes/trends that can be validated by SMEs and act as a workforce multiplier." }, { "tracker_id": "hhs-2025-bdddb6a77d", "fiscal_year": "FY2025", "use_case_name": "Medicare Part C/D Marketing Material Review", "previous_name_reported": "", "bureau_as_reported": "HHS/CMS/OPOLE", "component": "CMS", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Expand the volume of materials reviewed while providing consistent insights into trends and issues with materials received." }, { "tracker_id": "hhs-2025-f6d127845e", "fiscal_year": "FY2025", "use_case_name": "AI to Improve Public Access to the Administrative Appeals Process", "previous_name_reported": "", "bureau_as_reported": "HHS/DAB/OS", "component": "Office of the Secretary", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "More effectively leverage online resources to better assist the public and reduce the number of misfiled appeals submitted to the Departmental Appeals Board (DAB) via electronic filing." }, { "tracker_id": "hhs-2025-7440e64853", "fiscal_year": "FY2025", "use_case_name": "AI Use Policy Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/DAB/OS", "component": "Office of the Secretary", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Other", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Ensure public confidence in the integrity of DAB decisions by maximizing quality review standards and reducing errors." }, { "tracker_id": "hhs-2025-ebcefd46dc", "fiscal_year": "FY2025", "use_case_name": "Resources to Assist the Advisory Board In Identifying AI Tools for Use In An Adjudication Environment", "previous_name_reported": "", "bureau_as_reported": "HHS/DAB/OS", "component": "Office of the Secretary", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Other", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Agency adjudication activities require the analysis of large quantities of data to conduct docket analysis, identify efficiencies in case processing, and conduct 508 compliance required to make decisions available to the public." }, { "tracker_id": "hhs-2025-ab3d25a605", "fiscal_year": "FY2025", "use_case_name": "LLM-Assisted VAERS Analyses", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CBER", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Extraction of relevant information, tabulation of data, find patterns across adverse events reports, and generate hypotheses for further investigations." }, { "tracker_id": "hhs-2025-78b33d2dc6", "fiscal_year": "FY2025", "use_case_name": "Process Large Amount of Submitted Docket Comments", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CBER", "component": "FDA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Deduplication of public comments, and generating draft sentiment analysis and grouping of comments" }, { "tracker_id": "hhs-2025-fedfe6fd81", "fiscal_year": "FY2025", "use_case_name": "HIVE AI Pilot", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CBER", "component": "FDA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI is intended to assist in solving several problems related to the review process for INDs. Specifically, it aims to address issues of inefficiency, delays in identifying deficiencies and information overload. By providing recommendations for review disciplines, it helps reviewers to quickly identify potential review disciplines that are required. By identifying grossly deficient submissions early on, it reduces the workload and highlighting key data helps reviewers to focus on higher-level tasks that require their expertise." }, { "tracker_id": "hhs-2025-2f3d658153", "fiscal_year": "FY2025", "use_case_name": "AI and Vaccine Labeling", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CBER", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The app is designed to streamline vaccine label review processes, offering several key features to simplify and improve efficiency. It includes MedDRA integration for term searches, comprehensive search across all vaccine documents, and vaccine-specific information retrieval. Additionally, the app provides lookup functionality for approval timelines and active ingredients, as well as tools for detecting duplicates and comparing section content using AI-enhanced technology." }, { "tracker_id": "hhs-2025-34d9298353", "fiscal_year": "FY2025", "use_case_name": "356H Machine Learning (ML) Facility Supply Chain Role Classification", "previous_name_reported": "356H ML Facility Supply Chain Role Classification", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Manual classification of facility supply chain roles from FDA industry submissions (356h forms) is time-consuming and inefficient" }, { "tracker_id": "hhs-2025-838901163f", "fiscal_year": "FY2025", "use_case_name": "Risk-based FAR Review & Decision Support", "previous_name_reported": "Field Alert Reports (FAR) Prioritization Model", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Manual and subjective assessment process for Field Alert Reports (FARs) can lead to inconsistency in prioritizing and assessing reports, potentially leading to ineffective resource allocation where the same level of formality is being applied for all issues, regardless of risk." }, { "tracker_id": "hhs-2025-644ea76586", "fiscal_year": "FY2025", "use_case_name": "Analytics-Driven Supplement Evaluation (ASE)", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Exponential increase in post-approval chemistry, manufacturing, and controls (CMC) change submissions, with 80% being Changes Being Effected (CBE-30/0) notifications that may be suitable for systematic analytics-driven evaluation." }, { "tracker_id": "hhs-2025-8503c4dd02", "fiscal_year": "FY2025", "use_case_name": "FAR-based Facility Signal Detection Tool", "previous_name_reported": "Post-market Surveillance Reports Signal Detection and Cluster Analysis.", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Need for proactive detection of quality signals in post-market surveillance reports using statistical process control and topic modeling to identify potential drug quality hazards and mitigate the associated risks." }, { "tracker_id": "hhs-2025-ae874184ef", "fiscal_year": "FY2025", "use_case_name": "MedWatch Dashboard", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Need to proactively identify emerging issues and clusters within MedWatch reports using advanced analytics." }, { "tracker_id": "hhs-2025-93c04fd14a", "fiscal_year": "FY2025", "use_case_name": "Quality Surveillance Dashboard (QSD)", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Need for consistent, data-driven assessment of drug manufacturing facilities and proactive detection of potential quality signals that could indicate problems with drug safety or effectiveness." }, { "tracker_id": "hhs-2025-720d0829b3", "fiscal_year": "FY2025", "use_case_name": "Annual Report CMC", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "FDA receives numerous annual reports from drug manufacturers containing important manufacturing and quality information, but key details can be difficult to locate quickly within lengthy documents." }, { "tracker_id": "hhs-2025-baeb1298b7", "fiscal_year": "FY2025", "use_case_name": "Application References", "previous_name_reported": "Application-DMF Reference", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "FDA receives extensive drug application submissions that contain valuable references to related applications, but these relationships aren't always captured in existing regulatory databases." }, { "tracker_id": "hhs-2025-8064b2aa92", "fiscal_year": "FY2025", "use_case_name": "Extracting DMF Facilities from unstructured documents", "previous_name_reported": "DMF (Drug Master File) Facilities", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "FDA receives Drug Master File (DMF) submissions containing valuable information about manufacturing facilities, but this data exists in various document formats that require manual review to compile comprehensive facility information." }, { "tracker_id": "hhs-2025-8fc9aef593", "fiscal_year": "FY2025", "use_case_name": "Information Visualization Platform (InfoViP) to Support Analysis of FAERS safety reports", "previous_name_reported": "Information Visualization Platform (InfoViP) to Support Analysis of adverse event reports", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "The AI is intended to support the analysis of FAERS Individual Case Study Reports in post-market safety surveillance by automating duplicate detection, creating temporal visualizations, and classifying reports by information quality." }, { "tracker_id": "hhs-2025-8a88762ea3", "fiscal_year": "FY2025", "use_case_name": "Module 3 Facilities Extraction", "previous_name_reported": "Module 3 Faculties", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "FDA receives drug application submissions (ANDA, NDA, BLA) that contain important manufacturing facility information in Module 3 documents, but this data requires manual review to identify and organize all facility details." }, { "tracker_id": "hhs-2025-57ce4cbf34", "fiscal_year": "FY2025", "use_case_name": "Packaging Materials and Suppliers", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "FDA needs to efficiently identify which Drug Master Files (DMFs) contain specific packaging materials and understand how these materials connect to drug applications, but this information is currently difficult to locate across numerous documents." }, { "tracker_id": "hhs-2025-130b6749ef", "fiscal_year": "FY2025", "use_case_name": "Real World Data/Evidence", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "FDA receives numerous submissions containing real-world data and evidence, but identifying and cataloging these studies across various submission types is time-intensive and critical for regulatory reporting requirements under PDUFA (Prescription Drug User Fee Act)." }, { "tracker_id": "hhs-2025-54e606aff0", "fiscal_year": "FY2025", "use_case_name": "Regulatory Starting Material", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "FDA needs comprehensive visibility into the upstream supply chain for drug manufacturing, particularly tracking regulatory starting materials and their suppliers across approved and pending drug applications to better understand potential supply chain vulnerabilities." }, { "tracker_id": "hhs-2025-b210395926", "fiscal_year": "FY2025", "use_case_name": "Resource Capacity Planning", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Other", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "FDA needs to accurately predict the volume and complexity of incoming drug application submissions to ensure appropriate staffing and resources are available for timely reviews under the user fee program" }, { "tracker_id": "hhs-2025-ba32c85e72", "fiscal_year": "FY2025", "use_case_name": "CDER Publications", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Inefficient manual curation and categorization of publications by CDER authors. It aims to automate the process of organizing publications by focus areas for data call responses and identifying regulatory publications, replacing time-consuming manual review processes." }, { "tracker_id": "hhs-2025-b62eecf30d", "fiscal_year": "FY2025", "use_case_name": "CDER Regulatory Science Research (RSR) Projects AI for Process Control in Advanced Manufacturing", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Need for better process control in continuous manufacturing and development of soft sensors for real-time release testing strategies." }, { "tracker_id": "hhs-2025-44146224fe", "fiscal_year": "FY2025", "use_case_name": "Creating a development network", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI is intended to solve the problem of inconsistent data formats and inefficient access to unstructured clinical data across multiple healthcare sites. It aims to standardize EHR and claims data conversion into the Sentinel Common Data Model and develop processes for storing and extracting metadata from free text notes to enable timely execution of future Sentinel surveillance tasks." }, { "tracker_id": "hhs-2025-06501819da", "fiscal_year": "FY2025", "use_case_name": "Developing an Objective and Quantitative Endpoint for Atopic Dermatitis in Pediatric and Adult Populations", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Intended to solve the problem of lacking objective, quantitative methods to assess nocturnal scratching in children with atopic dermatitis. It aims to create a digital endpoint that can accurately measure scratching behavior to evaluate the efficacy and performance of FDA-regulated treatments for atopic dermatitis and pruritus, addressing an unmet need in clinical assessment." }, { "tracker_id": "hhs-2025-df83b43067", "fiscal_year": "FY2025", "use_case_name": "Drug Shortage Predictive Model", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Drug shortages have increased significantly since 2017 and worsened during COVID-19, creating critical gaps in patient access to essential medications. FDA seeks to develop predictive capabilities to anticipate shortages before they occur." }, { "tracker_id": "hhs-2025-629e1bae85", "fiscal_year": "FY2025", "use_case_name": "Category Subcategory Classification - Safety Reports Bot", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Other", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Manual analysis and data entry of safety report submissions is time-intensive and requires staff to review scanned PDFs and determine appropriate categories." }, { "tracker_id": "hhs-2025-f4472b9c3e", "fiscal_year": "FY2025", "use_case_name": "Data Extraction from IND Safety Reports using OCR/AI Technologies", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Manual extraction of data from IND safety reports is labor-intensive and time-consuming for regulatory staff." }, { "tracker_id": "hhs-2025-7f2f7cd19b", "fiscal_year": "FY2025", "use_case_name": "AI Editing Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This AI model is designed to detect style and formatting inconsistencies in FDA draft documents by comparing them against the FDA CDER Style Guide standards. It solves the problem of manual quality control inefficiencies and ensures consistent adherence to established documentation standards across all FDA CDER publications." }, { "tracker_id": "hhs-2025-350c47afce", "fiscal_year": "FY2025", "use_case_name": "Use case package 1: Empirical application of the Sentinel EHR and claims Data Partner network to address ARIA insufficient inferential requests (UC1)", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This AI project is designed to solve the problem of determining whether available data sources and analytical methods are suitable for specific pharmacoepidemiologic research questions. It aims to systematically evaluate data fitness-for-purpose and identify viable use cases where protocol-based studies can reliably assess drug safety and effectiveness in real-world populations." }, { "tracker_id": "hhs-2025-671a1674ed", "fiscal_year": "FY2025", "use_case_name": "CI5: Development and refinement of toolkits for routine use in the EHR and claims Data Partner network", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This project is designed to solve the problem of inconsistent or inefficient data analysis capabilities across the EHR and claims Data Partner network. It aims to create standardized, reliable analytical tools that can be routinely deployed across different data partners to improve the consistency, quality, and efficiency of pharmacoepidemiologic analyses within the Sentinel System." }, { "tracker_id": "hhs-2025-ae93929be5", "fiscal_year": "FY2025", "use_case_name": "Use case package 2 (UC2): Empirical application of the Sentinel EHR and claims Data Partner network to enhance ARIA insufficient inferential requests and atypical descriptive requests", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This AI project is designed to solve the problem of translating theoretical innovative methods into practical, real-world applications within the Innovation Center (IC) development network. It aims to create concrete, evidence-based examples that demonstrate how new technologies and approaches can be effectively implemented to address specific pharmacoepidemiologic and drug safety surveillance challenges." }, { "tracker_id": "hhs-2025-a8ae1c85e2", "fiscal_year": "FY2025", "use_case_name": "FE5: Incorporate a range of frequently used engineering features from EHRs into the Sentinel common data model in the Sentinel EHR and claims linked Data Partner network", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This AI project is designed to solve the problem of extracting valuable clinical information trapped in unstructured free-text fields within electronic health records. It aims to create a systematic feature engineering approach that can convert narrative clinical notes and text data into structured, analyzable formats for pharmacoepidemiologic research and drug safety surveillance." }, { "tracker_id": "hhs-2025-22e04db0c4", "fiscal_year": "FY2025", "use_case_name": "Develop an empirical algorithm to automate negative control identification in Sentinel System using the “Data-driven Automated Negative Control Estimation (DANCE)” algorithm", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This AI project is designed to solve the problem of optimizing the Data-driven Automated Negative Control Estimation (DANCE) algorithm for real-world implementation in large electronic healthcare database studies. It aims to use plasmode simulation to refine the algorithm's performance and then validate the tailored approach through a multisite test case focused on safety endpoint detection, ensuring the method works effectively across different healthcare data environments." }, { "tracker_id": "hhs-2025-780a66ce0a", "fiscal_year": "FY2025", "use_case_name": "Support tools that can be used in conjunction with Electronic Health Record (EHR) data, such as machine learning and natural language processing (NLP), and the use of Artificial Intelligence (AI) chart review tools", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This AI project is designed to solve the problem of rapidly responding to urgent or emerging drug safety signals that require immediate attention and coordinated action. It aims to leverage key expertise and resources at the Sentinel Operations Center to quickly address time-sensitive safety concerns that may pose risks to public health." }, { "tracker_id": "hhs-2025-ca67f113ba", "fiscal_year": "FY2025", "use_case_name": "Sentinel System Task Order to address an Emerging Safety Need", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This AI project is designed to solve the problem of efficiently validating emerging safety signals through chart review when traditional structured data is insufficient. It aims to use NLP-supported tools to extract and analyze information from unstructured clinical notes, enabling faster and more comprehensive chart abstraction and adjudication processes for urgent safety investigations." }, { "tracker_id": "hhs-2025-ea46d45ade", "fiscal_year": "FY2025", "use_case_name": "FOIA REDACTION (FRED) TOOL", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "FRED is designed to support FOIA staff in redacting records more efficiently and consistently. FOIA redaction can be a time-consuming process and experience large back-logs of requested documents. It aims to use AI to analyze, identify, and generate predictions of text for redaction, thereby improving the efficiency of FOIA response processing." }, { "tracker_id": "hhs-2025-ad497740bf", "fiscal_year": "FY2025", "use_case_name": "AI for Regulatory Review (AIRR) Pilot", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI-assisted regulatory review paradigm addresses the inefficiency and administrative burden of manually searching through vast amounts of disconnected sponsor-submitted and FDA-generated documents by integrating three components: AI-powered prompt engineering for streamlined workflows, real-time regulatory data retrieval systems, and automated document formatting capabilities that maintain human oversight while significantly enhancing review efficiency and consistency." }, { "tracker_id": "hhs-2025-9a9c52e554", "fiscal_year": "FY2025", "use_case_name": "AI-assisted Platform for Clinical Pharmacology Review", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI solution addresses inefficiency in the clinical pharmacology review process by reducing time reviewers spend on routine tasks, allowing them to focus their expertise on complex scientific analysis and decision-making that truly requires their specialized knowledge." }, { "tracker_id": "hhs-2025-a0c079f7e7", "fiscal_year": "FY2025", "use_case_name": "CoreDF", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Other", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Current nonclinical review processes require manual extraction and analysis of sponsor findings from lengthy PDF study reports, creating inefficiencies in data quality assessment and regulatory timeline." }, { "tracker_id": "hhs-2025-0e1e18efa8", "fiscal_year": "FY2025", "use_case_name": "AI for Bioanalytical Study Risk Assessment and Inspection Readiness", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Challenges in assessing large amounts of analytical/bioanalytical study information for risk assessment and inspection preparation in a short period of time" }, { "tracker_id": "hhs-2025-e9d0d7e413", "fiscal_year": "FY2025", "use_case_name": "AI for Clinical Study Risk Assessment and Inspection Preparation", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Challenges in assessing large amounts of clinical study information for risk assessment and inspection planning in a short period of time" }, { "tracker_id": "hhs-2025-33932822a9", "fiscal_year": "FY2025", "use_case_name": "AI for Assessing Bioanalytical Study Conduct Alignment with Guidance and Method SOPs", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Cross-comparing large amounts of bioanalytical data (reports, validation, tabulated data) with the M10 guidance and aligned method Standard Operating Procedures (SOPs) to identify all possible study conduct issues." }, { "tracker_id": "hhs-2025-871cf66ff4", "fiscal_year": "FY2025", "use_case_name": "SCANS Facility Role Predictor", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI was used to identify facilities that are either Active Pharmaceutical Ingredient (API) or Finished Dosage Form (FDF) manufacturers that were either missed or misclassified in regulatory tracking systems." }, { "tracker_id": "hhs-2025-9c63c4f13d", "fiscal_year": "FY2025", "use_case_name": "Document Room Submission AI-Assisted Categorization", "previous_name_reported": "Document Room Submission Auto-categorization", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Current document room submission categorization process is very manual and costly, impacting end-to-end regulatory review acceleration by creating bottlenecks, increasing processing times, and reducing overall efficiency in the review workflow." }, { "tracker_id": "hhs-2025-75a72c890f", "fiscal_year": "FY2025", "use_case_name": "AI-Assisted Drug Review Letter Drafting", "previous_name_reported": "Drug Review Letter Generation using GenAI", "bureau_as_reported": "HHS/FDA/CDER", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This approach to AI-assisted document drafting provides faster implementation with minimal technical requirements, future-proofs document generation capabilities, reduces ongoing maintenance costs, and eliminates technical complexity by replacing code-heavy solutions with dynamic AI prompts." }, { "tracker_id": "hhs-2025-e3bb3172c3", "fiscal_year": "FY2025", "use_case_name": "Supply Chain Resilience Program, Office of Supply Chain Resilience (OSCR) - Foresight", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDRH", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Estimating potential future demand of medical devices" }, { "tracker_id": "hhs-2025-2e7649d32d", "fiscal_year": "FY2025", "use_case_name": "Medical Data Enterprise Artificial Intelligence (MDE AI)", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDRH", "component": "FDA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Create efficiencies in the regulatory review processes for medical devices; reduce administrative burden to staff and allow them to focus their expertise on scientific and clinical work and not administrative processes" }, { "tracker_id": "hhs-2025-34fbb680dc", "fiscal_year": "FY2025", "use_case_name": "COMET (Consult Memo Assistant)", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/CDRH", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Other", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Efficiency of the premarket regulatory review" }, { "tracker_id": "hhs-2025-cee30cacd2", "fiscal_year": "FY2025", "use_case_name": "Food AI Decision Engine (FAIDE)", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/HFP", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Prioritize limited regulatory resources and maximize public health protection." }, { "tracker_id": "hhs-2025-c2ca751df1", "fiscal_year": "FY2025", "use_case_name": "Warp Intelligent Learning Engine (WILEE)", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/HFP", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Identify emerging chemical signals and violative food substances by analyzing a large data set in a fraction of the time that it would have taken scientific reviewers to analyze the publications." }, { "tracker_id": "hhs-2025-bd99a70592", "fiscal_year": "FY2025", "use_case_name": "Rapid Intuitive Pathogen Surveillance (RIPS)", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/HFP", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [ "surveillance", "laboratory" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Identify and prioritize incoming sources of potential foodborne outbreaks, maximizing public health by reducing the time burden on regulators." }, { "tracker_id": "hhs-2025-11b1f857a3", "fiscal_year": "FY2025", "use_case_name": "AI-Powered Assistant for Pathogen Detection (AIPD)", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/HFP", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AIPD is designed to address several key challenges in data analysis for foodborne pathogens, such as accessibility barriers for data sources, manual workflow overhead, knowledge gaps in tool selection, and complex project management." }, { "tracker_id": "hhs-2025-79d1c09e36", "fiscal_year": "FY2025", "use_case_name": "Product Label and Text Extraction System (PLATES)", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/HFP", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Manual food label data extraction causes slow data accessibility and insights. Manual data processing and standardization causes slow data accessibility and insights. Decentralized food label data limits research and regulatory processing of industry compliance and health impacts." }, { "tracker_id": "hhs-2025-9f2f28d998", "fiscal_year": "FY2025", "use_case_name": "Data Ingestion and Content Explorer (DICE)", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/HFP", "component": "FDA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Multiple stakeholders across HFP have the business need to search for content within artifacts and documents uploaded to various systems. For systems like CARA and CARTS, document search capabilities are limited due to the Appian technology stack utilized by these systems. As the Human Food Data Platform continues to grow, it will also need to provide SMEs with the capability to search within the data platform. Offices need a quicker way to search for content within documents and databases to find relevant data across a multitude of use cases including regulatory and compliance reviews, outbreak response investigation, and research tracking and administration. Additionally, multiple HFP offices have business processes requiring extracting structured data from unstructured documents for data analysis, regulatory reviews, and other business intelligence insights which are currently supported through manual operations. DICE will enable SMEs to obtain properly formatted structured data from unstructured data sources." }, { "tracker_id": "hhs-2025-d257263110", "fiscal_year": "FY2025", "use_case_name": "Smart Solution for Docket Management (SSDM)", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/OC", "component": "FDA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Other", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI is intended to solve the extremely labor-intensive and time consuming process of manually collating, de-duplicating, and categorizing public comments on FDA dockets." }, { "tracker_id": "hhs-2025-d34270b565", "fiscal_year": "FY2025", "use_case_name": "Elsa GenAI Chat Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/OC", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Other", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Elsa is designed to support FDA employees by providing clear, accurate information and assistance with work-related tasks. Elsa's primary purpose is to help streamline information access and decision-making processes within the FDA context." }, { "tracker_id": "hhs-2025-5be818f8d7", "fiscal_year": "FY2025", "use_case_name": "AI-Assisted Systematic Review and Validation of Analytical Worksheets", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/OCS", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Law Enforcement", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Manual review and validation of analytical worksheets can create bottlenecks in regulatory processes, introduces potential human error, and limits scalability of quality assurance procedures across FDA operations. Analytical worksheets serve as critical evidence in legal proceedings, enforcement actions, and regulatory decisions affecting public health and safety. These worksheets are the legal documents that will be used and referenced in a court of law and legal proceedings if FDA determines regulatory action should be taken in accordance with the Federal Food, Drug, and Cosmetic Act and subsequent amending supplements codified in Title 21of the United States Code." }, { "tracker_id": "hhs-2025-3b20f7ec93", "fiscal_year": "FY2025", "use_case_name": "AI-Generated Data Processing and Visualization Code Development", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/OCS", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Manual coding for data processing and visualization creates barriers to AI adoption, requires specialized expertise not available across all teams, and limits agency ability to maximize value of existing data investments as directed by OMB M-25-21." }, { "tracker_id": "hhs-2025-bb8b8b3a0f", "fiscal_year": "FY2025", "use_case_name": "AI-Enhanced FDA Regulated Commodity Consumption Pattern Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/OCS", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Traditional analytical methods for determining consumption patterns of FDA-regulated commodities are limited in scope and processing speed which hinders comprehensive market surveillance, trend analysis, and evidence-based regulatory science research necessary for informed policy development." }, { "tracker_id": "hhs-2025-9880870029", "fiscal_year": "FY2025", "use_case_name": "AI-Enhanced High-Dimensional Matrix Dataset Trend and Correlation Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/OCS", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "High-dimensional datasets present analytical challenges that exceed traditional statistical methods' capabilities, limits the ability to extract meaningful insights from complex regulatory data structures, and hinders evidence-based regulatory science advancement." }, { "tracker_id": "hhs-2025-9f7e13e678", "fiscal_year": "FY2025", "use_case_name": "ChatBot for Safety Reporting Portal Adverse Events and Product problems submissions", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/ODT", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "NLP", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Increases data integrity and aid with routing the user to the correct commodity group to submit their adverse event or product problem. DRUID AI, a COTS tool, boasts a comprehensive suite of functionalities, encompassing conversational flows and seamless diverse data sources integrations such as SQL, ServiceNow, UiPath, API, and knowledge base services. Its sophisticated Natural Language Processing and Understanding capabilities empower precise interpretation of user queries across various languages and dialects." }, { "tracker_id": "hhs-2025-c89abab8f7", "fiscal_year": "FY2025", "use_case_name": "Machine Learning as a Service: Translate and extract text from images using AI", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/ODT", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This AI project is designed to solve the problem of reviewing foreign products, extracting ingredient lists, and border inspections" }, { "tracker_id": "hhs-2025-3a64aa21d3", "fiscal_year": "FY2025", "use_case_name": "Machine Learning as a Service: Extract data from product labels, business forms, and image files", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/ODT", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This AI project is designed to assist in capturing data submitted to & reviewed by the FDA by extracting text and data, determining its structure, and saving the information in a more useful format" }, { "tracker_id": "hhs-2025-7aa8fb0813", "fiscal_year": "FY2025", "use_case_name": "Filer Evaluation prioritization using risk-based decision Machine Learning approach", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/OII", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The FDA's Office of Information Operations (OIO) has an opportunity to enhance its evaluation capabilities across over 4,000 filers in the current FDA inventory by implementing a systematic, data-driven approach to risk assessment and prioritization. By developing standardized evaluation processes and integrated analytical tools, OIO can optimize resource allocation, improve consistency in risk identification, and strengthen the FDA's capacity to effectively protect public health through targeted regulatory oversight." }, { "tracker_id": "hhs-2025-a5e3cdccff", "fiscal_year": "FY2025", "use_case_name": "Electronic translational services for regulatory documents for articles offered for import", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/OII", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "FDA' Office of Information Operations (OIO) seeks to implement automated translation capabilities for foreign language documents essential to import operations. By integrating translation services directly into OIO staff workflows, the Agency can improve import screening to better serve stakeholders while maximizing staff capacity for core public health protection activities." }, { "tracker_id": "hhs-2025-e9869e3de9", "fiscal_year": "FY2025", "use_case_name": "AI-Powered Video Analytics for Law Enforcement", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/OII", "component": "FDA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "Law Enforcement", "ai_type_as_reported": "Computer Vision", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI is intended to solve the challenge of manually reviewing large volumes of surveillance video, which is time-consuming, labor-intensive, and prone to human error." }, { "tracker_id": "hhs-2025-20edd5ac29", "fiscal_year": "FY2025", "use_case_name": "Computer Vision to translate and mine Product Labeling photos to analyze labeling for potential violations", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/OII", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Computer Vision", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The intent is to reduce the amount of time import operations users spend reviewing product labeling of imported products for violations." }, { "tracker_id": "hhs-2025-973b98aea8", "fiscal_year": "FY2025", "use_case_name": "Intelligent Document Processing to analyze current import entry documentation for potential discrepancies", "previous_name_reported": "", "bureau_as_reported": "HHS/FDA/OII", "component": "FDA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The intent is to assist in the process of discrepancy identification between documentation submitted by trade and CBP line data submitted." }, { "tracker_id": "hhs-2025-572eca92b4", "fiscal_year": "FY2025", "use_case_name": "AI Audit Resolution Assistant (AIARA)", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Other", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The DFI PRF AIARA project leverages AI to streamline the single audit process. The team utilizes the Robotic Process Automation (RPA) to extract data from the Federal Audit Clearinghouse to create templates for both the audit notification and management decision letters, and to generate a comprehensive report of the auditee and its findings." }, { "tracker_id": "hhs-2025-164e04205a", "fiscal_year": "FY2025", "use_case_name": "Knowledge Navigator", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "The objective is to develop an AI model that can answer detailed and complex questions about the key programmatic document, Application and Program Guidance (APG), that is issued annually before the application cycle opens. The PoC LLM has Applicant and Program Guidance (APG) documents for 10 loan repayments and scholarship programs." }, { "tracker_id": "hhs-2025-17ea32607f", "fiscal_year": "FY2025", "use_case_name": "Medical records summarization", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI will reduce the amount of time required to review and evaluate a claim." }, { "tracker_id": "hhs-2025-3bb86647ee", "fiscal_year": "FY2025", "use_case_name": "NOFO Compliance Assistant", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Other", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "HRSA leadership would benefit from an automated solution to evaluate HRSA Notice of Funding Opportunities (NOFO) against dynamically-changing Executive Orders (EO) and OMB memos to ensure NOFO compliance with White House priorities." }, { "tracker_id": "hhs-2025-ff0768663e", "fiscal_year": "FY2025", "use_case_name": "PRF Program Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Answer the inquiries related to GAO, FOIAs, and Litigation quickly and efficiently" }, { "tracker_id": "hhs-2025-dd2c866f62", "fiscal_year": "FY2025", "use_case_name": "Scholar Match", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "The current candidate evaluations and placement process is complex and challenging for both analysts and participants. By enhancing this process with AI/ML support could optimize resource allocation and candidate satisfaction, significantly impacting workforce distribution and efficiency in critical health areas." }, { "tracker_id": "hhs-2025-8f84bd0cd5", "fiscal_year": "FY2025", "use_case_name": "Scholarship Insight", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "The NHSC and Nurse Corps Scholarship reviewers need to receive and score thousands of essays submitted and the manual process delays the application processes. We are looking forward to building a Generative AI program that looks at all of the previous essays and how the scoring rubric was done and train it to do the first cut at scoring the essays. This could enhance the fairness and efficiency of scholarship evaluations, ensuring a thorough review process that supports equitable student opportunities." }, { "tracker_id": "hhs-2025-71498c096b", "fiscal_year": "FY2025", "use_case_name": "Site Application Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "The current application review process requires internal analysis to review many complex documents to determine Site Eligibility to employ a member of the NHSC or Nurse Corps. By enhancing this process with AI/ML support could revolutionize and streamline the NHSC, Substance Use Disorder Treatment and Recovery (STAR)and Nurse Corps Site Application Process." }, { "tracker_id": "hhs-2025-573b7ed18c", "fiscal_year": "FY2025", "use_case_name": "RefineAI - Enhanced Summary Statements", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Other", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "DIR would benefit from an automated system that would correct or solve grammatical issues, redundant comments, and acronym expansion at the merging stage within ARM to increase efficiency in panels and devote more time to discussion with hopes of more useful feedback without the pressure of time constraints due to editing. At least ten percent of overall ORC panel time is devoted to editing in real time for grammatical and duplicative issues. On average, discussion of an application takes 60 minutes; if we save 6 minutes of editing time per application, we can lower hourly rates for contractor support and lower hourly rates for editing post panel. In addition, we can shift focus in panel to content-specific discussion promoting higher quality feedback." }, { "tracker_id": "hhs-2025-74ad57e656", "fiscal_year": "FY2025", "use_case_name": "FOIA Exemption-Aligned Redaction", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The FOIA staff will leverage this solution to review records in response to FOIA requests, search the internet for publicly available information (names, email addresses, contact information of federal and non-federal individuals), and identify personally identifiable information (PII). The goal is to reduce the burden on the HRSA FOIA staff and process the requests quickly. Automating the manual process will save analysts time and effort and will help reduce/prevent human error. The FOIA staff reviews between 35-50 pages per hour, totaling 600 hours per month." }, { "tracker_id": "hhs-2025-7ec4e96c20", "fiscal_year": "FY2025", "use_case_name": "HRSA Data Warehouse ChatBot", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "HRSA Data Warehouse Chatbot to respond to public Inquiries." }, { "tracker_id": "hhs-2025-b335679464", "fiscal_year": "FY2025", "use_case_name": "Code Conversion for PowerBI Migration", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Generate code to support migration and conversion from Tableau to PowerBI to save license cost as well as migration labor costs" }, { "tracker_id": "hhs-2025-486fec9109", "fiscal_year": "FY2025", "use_case_name": "HRSA Fact Sheets", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Generate HRSA’s Program Fact Sheets for public use and reduce the cycle time by eliminating manual review and validation process." }, { "tracker_id": "hhs-2025-122d9fccb7", "fiscal_year": "FY2025", "use_case_name": "Program Reporting System Knowledge base Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Help users get relevant information quickly instead of in lengthy manuals, FAQs, or scattered documentation and reduce support cost." }, { "tracker_id": "hhs-2025-3742c6ffd6", "fiscal_year": "FY2025", "use_case_name": "Program Reporting System Natural Language Search", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Simplified Information availability for faster decision making and eliminate the maintenance cost for the legacy SOLRs search server." }, { "tracker_id": "hhs-2025-48241c9af9", "fiscal_year": "FY2025", "use_case_name": "AI identification of \"High-Risk\" Health Centers", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Manual identification of high-risk health centers is resource-intensive and may miss key indicators across large datasets. Would support a proactive, data-driven approach to inform site visit schedules and technical assistance planning." }, { "tracker_id": "hhs-2025-5b32efbb8d", "fiscal_year": "FY2025", "use_case_name": "Targeted Technical Assistance", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "AI will help identify the health centers/ geographic areas that will benefit the most from targeted technical assistance on clinical topics for improving quality metrics" }, { "tracker_id": "hhs-2025-e620fedd30", "fiscal_year": "FY2025", "use_case_name": "Program Compliance and Reporting Knowledge Base Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/HRSA", "component": "HRSA", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Users often need to navigate multiple technical assistance webpages, manuals, documents, FAQs, or submit inquiries through the contact form to find answers. This can be time-consuming and increases time and support capacity." }, { "tracker_id": "hhs-2025-813f2adfcc", "fiscal_year": "FY2025", "use_case_name": "NICHD RPAB AI/ML NICHD Relevance Model", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The primary objective is to enhance the efficiency, accuracy, and consistency of grant application referral assignments, while reducing the burden on Subject Matter Experts in RPAB. The AI system is expected to streamline the process of internal referral of new grant applications." }, { "tracker_id": "hhs-2025-21422c9637", "fiscal_year": "FY2025", "use_case_name": "Applying for Grants Chat Bot", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Save time for applicants by guiding users through the application process step-by-step, recommending additional resources for grant writing, and helping determine eligibility." }, { "tracker_id": "hhs-2025-39474ae5c4", "fiscal_year": "FY2025", "use_case_name": "Assisted Referral Tool (ART)", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This tool suggests which study sections an application might best fit for peer review. The applicant can then get a better idea of the topics being reviewed in that study section and the expertise the reviewers are likely to have. This tool is also used internally to help efficiently assign applications into study sections." }, { "tracker_id": "hhs-2025-616b5a37ce", "fiscal_year": "FY2025", "use_case_name": "Autism Spectrum Disorder (ASD) Classification Model for Children using Deep Neural Network", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-b2ed85a62d", "fiscal_year": "FY2025", "use_case_name": "Automated approaches for table extraction", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-a69b677827", "fiscal_year": "FY2025", "use_case_name": "Automated Basic-Applied Categorization of extramural grants", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "This machine-learning algorithm uses information about NIMH-funded research projects to categorize them as basic or applied research per the federal definitions for each." }, { "tracker_id": "hhs-2025-3b87f4d060", "fiscal_year": "FY2025", "use_case_name": "Biomedical Citation Selector (BmCS)", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Time-consuming human review of individual journal articles from multidisciplinary journals to determine inclusion in MEDLINE." }, { "tracker_id": "hhs-2025-21bc374cf5", "fiscal_year": "FY2025", "use_case_name": "Clinical Trial Predictor", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Applications that propose clinical trials that are submitted to NOFOs that do not allow clinical trials cannot be funded, no matter how well they do in review, because they were not reviewed using all appropriate clinical trial criteria. This application allows NIH to identify clinical trial applications submitted to NOFOs that do not allow clinical trials so they can be withdrawn before being reviewed and potentially transferred to a NOFO that does allow clinical trials." }, { "tracker_id": "hhs-2025-c7aea9d92c", "fiscal_year": "FY2025", "use_case_name": "ClinicalTrials.gov Protocol Registration and Results System Review Assistant", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "The quality control review process at ClinicalTrials.gov is time- and resource-intensive." }, { "tracker_id": "hhs-2025-fa3104316c", "fiscal_year": "FY2025", "use_case_name": "Collections Summarization Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Complex, time-consuming methods for discovery of content, and difficulty for users to understand the scope of content through summarization." }, { "tracker_id": "hhs-2025-df69d17012", "fiscal_year": "FY2025", "use_case_name": "CSR Public Chatbot (CPC)", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "This tool answers questions from the public, potential reviewers, and applicants by directly pulling relevant information from official government web pages, instead of searching FAQ lists." }, { "tracker_id": "hhs-2025-1f2aa98f9e", "fiscal_year": "FY2025", "use_case_name": "DAIT AIDS-Related Research Solution", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "DAIT POs need to identify grant applications that involve AIDS-related research (ARR) so they can evaluate them for additional funding." }, { "tracker_id": "hhs-2025-6548f0fc5f", "fiscal_year": "FY2025", "use_case_name": "Detecting Overlapping Science (DOS)", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "This tool detects applications that represent potential duplicate funding (funding the same research in different projects). It examines applications as they are submitted to the NIH and sends a report to relevant personnel in the agency." }, { "tracker_id": "hhs-2025-f0990d5988", "fiscal_year": "FY2025", "use_case_name": "Detection of Implementation Science focus within incoming grant applications", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "This tool uses natural language processing and machine learning to calculate an Implementation Science score that is used to predict if a newly submitted grant application proposes to use science that can be categorized as \"Implementation Science.\"" }, { "tracker_id": "hhs-2025-4835b87065", "fiscal_year": "FY2025", "use_case_name": "Expansion of Generative AI (GenAI) Caption Generation for all Collections Videos", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "High cost and low efficiency of video transcript and caption generation." }, { "tracker_id": "hhs-2025-8401dd31f2", "fiscal_year": "FY2025", "use_case_name": "Federal IT Acquisition Reform Act (FITARA) Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Contracting officers use this tool to help identify whether a Statement of Work meets the criteria of Federal IT Acquisition Reform Act (FITARA)." }, { "tracker_id": "hhs-2025-f177ed02af", "fiscal_year": "FY2025", "use_case_name": "Generative AI (GenAI) Still Image Tagging", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Other", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Lack of a cost-effective mechanism to label and describe digital images." }, { "tracker_id": "hhs-2025-76bf5e8db8", "fiscal_year": "FY2025", "use_case_name": "individual Functional Activity Composite Tool (inFACT)", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-2dbb1da19a", "fiscal_year": "FY2025", "use_case_name": "Internal Referral Module (IRM)", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Automated Assignment of Grants to Program Officers" }, { "tracker_id": "hhs-2025-b9b97d04ce", "fiscal_year": "FY2025", "use_case_name": "JIT Automated Calculator (JAC)", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "NIGMS requires extra justification to fund investigators whose total grant money exceeds $1.5M. Applicants are required to submit lengthy forms detailing all of their support and those of their key personnel. These forms are quite long and require quite a bit of time to read and tally up funding totals. This NLP searches the entire form and adds up all of the totals to help NIGMS program staff determine total funding for all key personnel of a grant application." }, { "tracker_id": "hhs-2025-2f4991d12f", "fiscal_year": "FY2025", "use_case_name": "LLM Support for Admin Services", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-d6940979f4", "fiscal_year": "FY2025", "use_case_name": "Mapping Sequence Data to Research Outcomes", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Effectively allocating resources needed to process, manage, and store a high volume of sequence data." }, { "tracker_id": "hhs-2025-7ba2ea80c2", "fiscal_year": "FY2025", "use_case_name": "Medical Text Indexer-NeXt Generation (MTIX) MEDLINE Indexing", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Time-consuming and burdensome manual indexing of MEDLINE citations" }, { "tracker_id": "hhs-2025-41a91af8ae", "fiscal_year": "FY2025", "use_case_name": "MicroStrategy Evaluation", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-ab17ae65f8", "fiscal_year": "FY2025", "use_case_name": "NanCI: Connecting Scientists", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "NanCI phone/web application to connect scientists. The app uses AI to match scientific content to users' interests. By collecting papers into a folder, a user can engage the tool to find similar articles in the scientific literature and can refine the recommendations by up or down voting recommendations. Users can also connect with others via their interests and receive and make recommendations via this social network. Users: Cancer Research Trainees at NCI and across the USA." }, { "tracker_id": "hhs-2025-0b8d4d4716", "fiscal_year": "FY2025", "use_case_name": "NBS Virtual Assistant", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Service Desk Operations" }, { "tracker_id": "hhs-2025-32de13a85b", "fiscal_year": "FY2025", "use_case_name": "NCI-DOE Collaboration, MOSSAIC project (Modeling Outcomes using Surveillance Data and Scalable AI for Cancer)", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "NLP", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "MOSSAIC applies deep learning natural language processing (NLP) and foundation models to population-based cancer data collected by NCI's Surveillance, Epidemiology, and End Results (SEER) program. DOE's Oak Ridge National Lab (ORNL) has data use agreements (DUAs) with multiple SEER registries to access and train models using SEER data. Two APIs are in production use in the data management system used by the SEER registries -- OncoID which predicts whether a pathology report is related to cancer and OncoIE which extracts key tumor characteristics from unstructured pathology report text. Together these APIs are an important part of moving the US towards near real-time cancer incidence reporting. In addition, a third API OncoMetsID, which predicts whether a pathology report is indicative of metastatic disease, is in a pilot phase to use in conjunction with other sources of information in the registries to identify recurrent disease." }, { "tracker_id": "hhs-2025-15b58ec318", "fiscal_year": "FY2025", "use_case_name": "NHLBI Chat", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "This is a generic AI chat tool that provides a secure chat interface (similar to public tools like ChatGPT) for NHLBI Chat. This tool enables staff to safely and securely explore how generative AI can be used on their sensitive (but non-PII/PHI) workloads." }, { "tracker_id": "hhs-2025-756a650440", "fiscal_year": "FY2025", "use_case_name": "NIAMS AI Chatbot Pilot", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To provide a secure, protected environment for NIAMS staff (OD, EP, IRP and IT) to explore, test, and understand how to use AI to be more efficient with a wide variety of administrative tasks such as general research, summarizing/querying documents, drafting emails, generating programming code, and creating presentation outlines?, etc." }, { "tracker_id": "hhs-2025-b17c700384", "fiscal_year": "FY2025", "use_case_name": "NICHD RPAB AI/ML Application Referral System", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "The primary objective is to enhance the efficiency, accuracy, and consistency of grant application referral assignments, while reducing the burden on Subject Matter Experts in RPAB. The AI system is expected to streamline the process of internal referral of new grant applications." }, { "tracker_id": "hhs-2025-d3ef7482c6", "fiscal_year": "FY2025", "use_case_name": "NLP Automated Referral", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Presumed high-impact, determined not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Referring applications manually is tedious and time consuming. Using an automated approach allows staff to focus their time on more difficult tasks." }, { "tracker_id": "hhs-2025-2ea1277c2e", "fiscal_year": "FY2025", "use_case_name": "OCIO GenAI Advisor", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-21a895f01d", "fiscal_year": "FY2025", "use_case_name": "OIT Help Desk Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "The OIT help desk would like to shorten resolution time for tickets, help empower users, generate a non-trivial response for a complex question in a desired format such as security, architecture, PMO policy, reports." }, { "tracker_id": "hhs-2025-a83db7300f", "fiscal_year": "FY2025", "use_case_name": "Person-level disambiguation for PubMed authors and NIH grant applicants", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "A person or entity may use several name variations on publications and/or grants, which causes uncertainty in attributing research contributions" }, { "tracker_id": "hhs-2025-77978ccf7d", "fiscal_year": "FY2025", "use_case_name": "Portfolio Analysis Summarization Tool (PAST)", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Reduce effort for summarization of custom research portfolios" }, { "tracker_id": "hhs-2025-0f45e3ca18", "fiscal_year": "FY2025", "use_case_name": "Program Classification Coding", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "To aid users, a solution has been developed that suggests the specific PCC that should be assigned to each application." }, { "tracker_id": "hhs-2025-77426ec2fd", "fiscal_year": "FY2025", "use_case_name": "RCDC AI Validation Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-92ad8326ab", "fiscal_year": "FY2025", "use_case_name": "Research Performance Progress Report (RPPR) Report Comparison", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Identification of duplication and overlap in grants year over year." }, { "tracker_id": "hhs-2025-0fa11917a9", "fiscal_year": "FY2025", "use_case_name": "Scientific summaries tool", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "Enhancing scientific summary development for communicating scientific achievement." }, { "tracker_id": "hhs-2025-b25971b2bb", "fiscal_year": "FY2025", "use_case_name": "Similarity-based Application and Investigator Matching (SAIM)", "previous_name_reported": "", 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"bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Computer Vision", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Automated screening of CryoEM specimens" }, { "tracker_id": "hhs-2025-d3df591995", "fiscal_year": "FY2025", "use_case_name": "Protein Modeling with AlphaFold", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Protein modeling" }, { "tracker_id": "hhs-2025-935fb42357", "fiscal_year": "FY2025", "use_case_name": "eSlate Bot", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This tool answers questions using genAI by pulling information directly from the eSlate nomination packages." }, { "tracker_id": "hhs-2025-7bc19ba53c", "fiscal_year": "FY2025", "use_case_name": "SRO Handbook Bot", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This tool answers Scientific Review Officer (SRO) questions by retrieving relevant information from the handbook, saving the user time looking for answers to their questions." }, { "tracker_id": "hhs-2025-3ed3d8eeea", "fiscal_year": "FY2025", "use_case_name": "Detection/Identification of Reviewer Expertise and Grant Application Content", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI is designed to detect and identify grant application content and reviewer expertise such as pediatric or other domains to support accurate matching and more efficient, informed decision-making" }, { "tracker_id": "hhs-2025-1ac48b2954", "fiscal_year": "FY2025", "use_case_name": "Detection of AI-Generated Reviewer Critiques", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This tool detects if a given written critique was generated by AI tools, such as large language models (LLMs), as the reviewers are not allowed to write their critiques using these AI assistants. Flagged critiques are examined by NIH staff and discussed with the reviewers." }, { "tracker_id": "hhs-2025-b5dab07eee", "fiscal_year": "FY2025", "use_case_name": "MirBot", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI tool addresses the challenge of efficiently and consistently processing incoming study PDFs by automatically extracting key information needed for grant review workflows." }, { "tracker_id": "hhs-2025-0bb654534a", "fiscal_year": "FY2025", "use_case_name": "Notebooks Hub", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Helping scientists and researchers code their applications, dashboards, and data analyses more quickly, have higher code standards, and gain more insight from their data" }, { "tracker_id": "hhs-2025-0df16e7f35", "fiscal_year": "FY2025", "use_case_name": "Ask Aithena", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Helps researchers stay on top of the latest research in their field and quickly get up to speed in new fields by providing conversational answers while also providing verifiable references for the user to read primary sources." }, { "tracker_id": "hhs-2025-32db0bd4c8", "fiscal_year": "FY2025", "use_case_name": "Writing code using AI", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Limited users able to develop code." }, { "tracker_id": "hhs-2025-39b13f9167", "fiscal_year": "FY2025", "use_case_name": "ChIRP - A ChatGPT Model for the NIH Intramural Community", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "We hoped to load text into the chatbot to assist in summarization and identification of prominent themes. ChIRP was moderately helpful for our use case, primarily because we used it at a stage in development where documents could not be directly uploaded into the chatbot" }, { "tracker_id": "hhs-2025-59e9c6c5df", "fiscal_year": "FY2025", "use_case_name": "Software Approval Agent", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "All software requests currently go to the IT Service Desk, who then need to research the approval status, respond to the end user, and forward the request to other offices in order to process, respond, and route the request to the ISSO or Administrative staff. The agent will help route software requests directly to the correct parties based on the approval status of the requested software." }, { "tracker_id": "hhs-2025-f163ffcb6e", "fiscal_year": "FY2025", "use_case_name": "LibreChat", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The NHGRI-internal generative chat and image capabilities of LibreChat will provide an alternative to using publicly available chat services that may expose NIH data. Prompt data is stored within NHGRI's system boundaries and will not be used to train public models." }, { "tracker_id": "hhs-2025-9f8d1d189e", "fiscal_year": "FY2025", "use_case_name": "Ethics AI Agent", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Users repeatedly ask Ethics staff the same type of question, which causes challenges for Ethics staff to manage their tasks effectively." }, { "tracker_id": "hhs-2025-e2b50e3fdb", "fiscal_year": "FY2025", "use_case_name": "Help Desk AI Assistant", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Help Desk staff need to write new responses to IT service desk requests and inquiries. The agent will provide Help Desk staff with ideal language when responding to tickets." }, { "tracker_id": "hhs-2025-17c8833159", "fiscal_year": "FY2025", "use_case_name": "Data Management and Sharing Plans - Assistant Review Tool", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This tool uses NLP to identify the areas in the grant application that deal with the Data Management and Sharing Plan (DMSP). It is designed to assist the reviewer in determining whether the application meets the NIH Data Management and Sharing requirement." }, { "tracker_id": "hhs-2025-c1c9cdca20", "fiscal_year": "FY2025", "use_case_name": "ITAC SOP Chat", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI provides natural language searching of ITAC SOP, policy, and as-built documents allowing users to easily locate knowledge and easily view the document section that includes the knowledge." }, { "tracker_id": "hhs-2025-f2ebee54e1", "fiscal_year": "FY2025", "use_case_name": "BDC Website Search", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI provides natural language searching of BDC website information, making it easier for users to find relevant information." }, { "tracker_id": "hhs-2025-54245b83e4", "fiscal_year": "FY2025", "use_case_name": "BioData Catalyst Harmonized Data Model", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Harmonizing complex scientific concepts in retrospectively collected data." }, { "tracker_id": "hhs-2025-113c0a130e", "fiscal_year": "FY2025", "use_case_name": "NHLBI Chat Workflow - Data Management and Sharing Plan", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Assists program officers in reviewing grant applications for a specific query in the checklist (DMSP)." }, { "tracker_id": "hhs-2025-2d8efb25c5", "fiscal_year": "FY2025", "use_case_name": "NHLBI Chat Workflow - Foreign Component", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Assists program officers in reviewing grant applications for a specific query in the checklist (Foreign Component)." }, { "tracker_id": "hhs-2025-412b2e0237", "fiscal_year": "FY2025", "use_case_name": "Merops", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Automate and expedite copyediting of scientific manuscripts" }, { "tracker_id": "hhs-2025-e4834bc70b", "fiscal_year": "FY2025", "use_case_name": "OSPIDA RPAB Scientific Coding Assistance Tool (CAT)", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "OSPIDA's Referral Program and Analysis Branch (RPAB) is responsible for scientific coding assignments of grant applications. They assign scientific codes to grants based on different objectives, selecting from a list of over 3,000 scientific codes related to NIAID's research areas." }, { "tracker_id": "hhs-2025-8b2d11bc59", "fiscal_year": "FY2025", "use_case_name": "AWS Exscribo", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Typical transcription services struggle to handle scientific and biomedical terminology, and many platforms do not include the ability to pretrain for specific words, or offer the ability to perform queries on that text in the transcription system itself." }, { "tracker_id": "hhs-2025-38cba05fde", "fiscal_year": "FY2025", "use_case_name": "Virtual Assistant for the NIAMS Grant Management Applications", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "New investigators and staff often struggle to locate policies, workflows, and troubleshooting steps buried across SharePoint guides, SOP documents, and in?app help screens for the two NIAMS Grants Management applications. In addition, support tickets and emails consume significant SME staff's time." }, { "tracker_id": "hhs-2025-cfbeaeca3a", "fiscal_year": "FY2025", "use_case_name": "Grant award process efficiency", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Reducing time burden of repetitive tasks related to the grants award process" }, { "tracker_id": "hhs-2025-072b55b104", "fiscal_year": "FY2025", "use_case_name": "Coding translation", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Translates code between programming languages, e.g. rewrite python script in equivalent R commands; translate natural language to code, e.g. write a python script that will convert this gene matrix to a transcript matrix etc." }, { "tracker_id": "hhs-2025-98de95c57a", "fiscal_year": "FY2025", "use_case_name": "Assist in research on the association between hearing loss and dementia for the NIDCD EARssentials hot topic presentation", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Assist in deepening my understanding the statistical methodologies applied in research examining the association between hearing loss and dementia." }, { "tracker_id": "hhs-2025-066981c4d9", "fiscal_year": "FY2025", "use_case_name": "AI-generated podcast describing recent advances in methods for analysis of RNA-seq data", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Genomic analysis is a rapidly evolving field with advances in analytical methods arising frequently. This makes it difficult to stay on top of new techniques." }, { "tracker_id": "hhs-2025-10bc537e68", "fiscal_year": "FY2025", "use_case_name": "AI-Enhanced Journal Clubs", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "A lack of engagement, efficiency, and productivity at journal clubs due to the burden of gathering topically relevant and high quality journal articles for review." }, { "tracker_id": "hhs-2025-6ee194b487", "fiscal_year": "FY2025", "use_case_name": "Grants Portfolio Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Facilitate scientific program management and grant portfolio assessment" }, { "tracker_id": "hhs-2025-3dd89cd1e0", "fiscal_year": "FY2025", "use_case_name": "Identifying pain vs non-pain research", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Identifying pain research studies is complicated because of the ubiquity of pain in language. NIH program staff working in the pain research field need to be able to distinguish pain research from opioid research, both of which get classified as \"Pain Research\" by RCDC. This usually requires significant investment of staff time, however, since a large enough training dataset has been developed we are trying to train various ML algorithms to be able to identify grants that are truly researching pain compared to grants that merely mention pain in their text." }, { "tracker_id": "hhs-2025-a26fbaf67a", "fiscal_year": "FY2025", "use_case_name": "HEAL Portfolio Topic Analysis", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The HEAL Initiative supports hundreds of pain research studies. It is meant to support pain research that cannot be carried out by an individual IC. One challenge we have is describing what HEAL research is. This project is an attempt to give program staff a starting point and visualization that describes the topics covered by HEAL research so that staff can start to understand the HEAL portfolio and how the different programs in HEAL are related or different. It will also allow staff to be able to explain how the HEAL portfolio is different from portfolios of different ICs. It may be possible for staff to carry out this analysis but using a ML algorithm would require a fraction of the staff time needed for this and be more consistent. While it may be possible for staff to classify the topics in the relatively small HEAL portfolio without AI, having a ML method in place will allow a single staff member to compare the HEAL portfolio to the much larger NIH portfolio using consistent methods." }, { "tracker_id": "hhs-2025-e9fce8590c", "fiscal_year": "FY2025", "use_case_name": "Determining how federal pain resarch has responded to the Federal Pain Research Strategy.", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The IPRCC has asked NIH to evaluate if all FPRS priorities are being addressed by NIH research. The task would require staff to categorize over 6000 grants into up to 13 FPRS priority areas. A single staff member can curate 5 grants per hour using these parameters, and agreement among staff members is approximately 30%. Therefore, we are carrying out this project using ML to be able to complete the project in a timely manner and without the need of significant staff time investment. It will identify areas of research that require staff to investigate further, instead of having staff curate the whole portfolio." }, { "tracker_id": "hhs-2025-6c0e81e10f", "fiscal_year": "FY2025", "use_case_name": "Transformer-Based Metadata Alignment Workflow", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Inconsistent data elements and differing definitions in glossaries of metadata structures across research data ecosystems hinder interoperability and FAIR-aligned reuse." }, { "tracker_id": "hhs-2025-a50aa08432", "fiscal_year": "FY2025", "use_case_name": "LLM-Assisted Referral Justification Email Generator", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Referral justifications are often time-consuming to draft manually and require consistent interpretation of referral guidelines and application content." }, { "tracker_id": "hhs-2025-b7927d831e", "fiscal_year": "FY2025", "use_case_name": "AI Assist", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This solution is designed to address the challenges of manually processing and analyzing large volumes of unstructured data—including documents, reports, and policies—which is often time-consuming, labor-intensive, and prone to human error." }, { "tracker_id": "hhs-2025-8a57791fa1", "fiscal_year": "FY2025", "use_case_name": "Portfolio analysis and grant summarizing program", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Extracts specific information from hundreds of grant applications in a consistent reproducable way" }, { "tracker_id": "hhs-2025-abc9375c22", "fiscal_year": "FY2025", "use_case_name": "RAG System For Travel Planning", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The chatbot answers questions from the users based on the user guide and potentially categorizing the IT helpdesk tickets." }, { "tracker_id": "hhs-2025-7e6ae54600", "fiscal_year": "FY2025", "use_case_name": "Generating Metadata for Web Archive Resources", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Time-intensive process needed to generate accurate and descriptive metadata about web resources" }, { "tracker_id": "hhs-2025-cb392fb208", "fiscal_year": "FY2025", "use_case_name": "Incorporating External Information into Taxonomy", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Time consuming and inefficient process of reading journal articles and manually copying new organism names from those articles into internal databases" }, { "tracker_id": "hhs-2025-061af370da", "fiscal_year": "FY2025", "use_case_name": "Using Llama to summarize PubMed Central (PMC) full text articles that contain information on protein function", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Inefficient and manual process to assign accurate function to proteins and protein models" }, { "tracker_id": "hhs-2025-150c488672", "fiscal_year": "FY2025", "use_case_name": "Leveraging NLP and LLMs to identify and characterize NIH prevention research via 160-topic taxonomy", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Health and Medical", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Semi-automate manual curation" }, { "tracker_id": "hhs-2025-a326bd94c7", "fiscal_year": "FY2025", "use_case_name": "Driving Efficiency and Expansion of Dietary Supplement Label Database Data through AI", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Enhance database QA and enhance database records acquisition rate" }, { "tracker_id": "hhs-2025-a16dd343b7", "fiscal_year": "FY2025", "use_case_name": "AI-enabled User Support and Impact Monitoring", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "User support for DSLD data inquiries and monitoring of DSLD usage and impact in the research community." }, { "tracker_id": "hhs-2025-84d142bc1b", "fiscal_year": "FY2025", "use_case_name": "Section 508 Azure OpenAI Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "This tool will streamline access to compliance resources, guidance, and support, empowering NIH staff and stakeholders to meet federal accessibility requirements. Designed to efficiently handle help desk inquiries, the chatbot will adhere to NIH’s security protocols and provide accurate, role-specific information." }, { "tracker_id": "hhs-2025-712a4f9dd4", "fiscal_year": "FY2025", "use_case_name": "Enhancing the RCDC with Generative AI", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Science", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The goal is to assess whether generative AI can enhance the RCDC process by minimizing time-consuming, resource-intensive routine and manual tasks and improving overall efficiency." }, { "tracker_id": "hhs-2025-33a0f85ea5", "fiscal_year": "FY2025", "use_case_name": "NIH Travel Policy AI Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The AI-powered chatbot is designed to address a key operational challenge: the high volume of routine travel policy inquiries directed toward NIH administrative staff. These questions, often repetitive and time-consuming, divert valuable human resources from more complex and strategic responsibilities.Specifically, the AI chatbot leverages Generative Artificial Intelligence (AI) to provide accurate, consistent, and real-time responses to questions related to the Federal Travel Regulation (FTR) and the NIH Travel Policy Handbook. By doing so, it significantly reduces the dependency on staff to manually research and craft responses to standard inquiries. In addition, the chatbot serves as the foundation for a self-service portal for the NIH community. This portal empowers employees and stakeholders to independently access authoritative travel policy guidance 24/7—improving efficiency, enhancing user experience, and ensuring policy compliance across the organization." }, { "tracker_id": "hhs-2025-c093e6b58c", "fiscal_year": "FY2025", "use_case_name": "PowerAutomate Delinquent Submissions", "previous_name_reported": "", "bureau_as_reported": "HHS/NIH", "component": "NIH", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Other", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Senior Auditors are required to spend significant time—approximately 25% of an FTE per auditor annually—on manually monitoring award recipients’ submission status, contacting recipients, providing oversight, and documenting compliance at multiple intervals leading up to the fiscal year end." }, { "tracker_id": "hhs-2025-19c46f95db", "fiscal_year": "FY2025", "use_case_name": "ABE (AI-driven. Beneficial. Efficient.)", "previous_name_reported": "", "bureau_as_reported": "HHS/OASH/OIDP", "component": "Office of the Secretary", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "By centralizing federal HIV program information in a custom Knowledge Base, ABE addresses the challenges of fragmented data and inefficient workflows, supporting operational efficiencies through a reduction in labor force and contracts, and providing the ability to quickly access, summarize, analyze, and create resources." }, { "tracker_id": "hhs-2025-7839803041", "fiscal_year": "FY2025", "use_case_name": "AI Harvest Service", "previous_name_reported": "", "bureau_as_reported": "HHS/OCIO", "component": "Office of the Secretary", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "The Harvest Service addresses inconsistent or incomplete metadata across distributed HHS open data sources by normalizing descriptions, creating concise summaries, and tagging data assets for improved searchability" }, { "tracker_id": "hhs-2025-99486ea6e6", "fiscal_year": "FY2025", "use_case_name": "Federal Assistant AI Agent", "previous_name_reported": "", "bureau_as_reported": "HHS/OCIO", "component": "Office of the Secretary", "stage_as_reported": "Pre-deployment", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Not high-impact", "topic_area_as_reported": "IT", "ai_type_as_reported": "Agentic AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "HHS and its Family of Agencies have a diverse, spread out basis of content that can be confusing to navigate and requires a user to have knowledge of HHS org structure to be able to obtain answers to their questions; leveraging Agentic AI using Turnkey app can reduce burden on citizens to find answers to their questions about HHS' services, and can reduce the volume of calls to HHS agency contact center representatives to answer common questions" }, { "tracker_id": "hhs-2025-99ca4657d1", "fiscal_year": "FY2025", "use_case_name": "ChatGPT", "previous_name_reported": "", "bureau_as_reported": "HHS/OCR", "component": "Office of the Secretary", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Law Enforcement", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Staffing shortage" }, { "tracker_id": "hhs-2025-064199c9be", "fiscal_year": "FY2025", "use_case_name": "CoPilot Outlook", "previous_name_reported": "", "bureau_as_reported": "HHS/OCR", "component": "Office of the Secretary", "stage_as_reported": "Pilot", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Not high-impact", "topic_area_as_reported": "Law Enforcement", "ai_type_as_reported": "Generative AI", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Staffing shortage" }, { "tracker_id": "hhs-2025-7574db74ec", "fiscal_year": "FY2025", "use_case_name": "Similar Opportunities (KNN)", "previous_name_reported": "", "bureau_as_reported": "HHS/OS/ASFR", "component": "Office of the Secretary", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Agencies do not receive the highest number of proposals from capable applicants as applications generally tend to come from the same set of applicants." }, { "tracker_id": "hhs-2025-41f57a874a", "fiscal_year": "FY2025", "use_case_name": "Applicant Help Chatbot", "previous_name_reported": "", "bureau_as_reported": "HHS/OS/ASFR", "component": "Office of the Secretary", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Service Delivery", "ai_type_as_reported": "NLP", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Answer user questions about Grants.gov" }, { "tracker_id": "hhs-2025-42ed1ee54c", "fiscal_year": "FY2025", "use_case_name": "DECIDE", "previous_name_reported": "", "bureau_as_reported": "HHS/SAMHSA", "component": "SAMHSA", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-bdcd241a53", "fiscal_year": "FY2025", "use_case_name": "Document drafting and editing", "previous_name_reported": "", "bureau_as_reported": "HHS/SAMHSA", "component": "SAMHSA", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-6ef906b9a7", "fiscal_year": "FY2025", "use_case_name": "Document summarization", "previous_name_reported": "", "bureau_as_reported": "HHS/SAMHSA", "component": "SAMHSA", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Not high-impact", "topic_area_as_reported": "Not reported", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "yes", "purpose_as_reported": "" }, { "tracker_id": "hhs-2025-efefc48dda", "fiscal_year": "FY2025", "use_case_name": "AWS Kendra Search tool", "previous_name_reported": "", "bureau_as_reported": "HHS/SAMHSA", "component": "SAMHSA", "stage_as_reported": "Deployed", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Not high-impact", "topic_area_as_reported": "Admin Functions", "ai_type_as_reported": "Classical ML", "public_health_functions": [], "name_in_fy2024_inventory": "no", "purpose_as_reported": "Kendra address the technical limitations of efficient access to public information." }, { "tracker_id": "hhs-2024-a1d3d56bad", "fiscal_year": "FY2024", "use_case_name": "Design Your Facility", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Office of Refugee Resettlement (ORR) manages many facilities for unaccompanied children and must regularly determine bed availability, configuration, and assignments. The current process for gathering this information takes lengthy training and assistance from system administrators. The Design Your Facility interface leverages LLMs to help users more quickly configure the correct building, room, and bed layout needed to create their facility’s “digital twin” in the UC Bed Network Facility Mapper without lengthy training or deskside assistance from system administrators." }, { "tracker_id": "hhs-2024-cd5736f5ed", "fiscal_year": "FY2024", "use_case_name": "Discover Financial Business Intelligence System (FBIS) Report Analysis (Sub-CAN Line Items)", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This project provides a resource for ACF Discover users in the budget community to identify related line items across disparate reports from FBIS. The process searches the data and aggregates information (e.g. supplier name, document number, total obligations) to create an up-to-date, real-time spend plan that pulls from the open-closed report, requisition purchase order report, and integrates with funds distribution report. AI returns line items from the open-closed report and requisition purchase order report related to the sub-CAN line item description such as supplier name, document number, document type, and total obligations for that sub-CAN line item. The Discover FBIS Report Analysis (Sub-CAN Line Items) LLM helps find related obligations and line items from various reports and returns information crucial for a budget officer when they are creating a spend plan for their office. The LLM aids ACF Discover track various line items and pulls the information into one place: the Spend Plan module where ACF can track yearly budgets and monitor budget health." }, { "tracker_id": "hhs-2024-bace34f1fa", "fiscal_year": "FY2024", "use_case_name": "Grant Spend Health Analysis", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The mission of the Office of Grants Management (OGM)is to provide high-quality fiscal stewardship and management for ACF-funded recipients across the country and in U.S. territories. This includes responsive oversight and technical assistance for discretionary awards, non-discretionary awards, and cooperative agreements. OGM seeks ways to prioritize proactive technical assistance to grant recipients based on likelihood that awarded funds may not be fully utilized. The goal is to identify still-active grants that are potentially on the path towards deobligating funding. This information would be visible to grant specialists within ACF. OGM anticipates developing customized training and technical assistance materials that can assist recipients if we predict that their grant is trending towards leaving funds unutilized." }, { "tracker_id": "hhs-2024-38eb1c9497", "fiscal_year": "FY2024", "use_case_name": "Structuring and Validating Completeness of Case Data Information", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "In accordance with the Ms L vs ICE settlement, the U.S. Customs and Border Protection (CBP) must provide specific pieces of information about family separations. Beginning November 2024, CPB will release this information. ACF's Office of Refugee Resettlement (ORR) plans to add family separation information to existing child profile information for reference. The AI output structures free form data into a more easily searchable format. These data will be used to assist in the placement process." }, { "tracker_id": "hhs-2024-269fe114ce", "fiscal_year": "FY2024", "use_case_name": "Triaging Notice of Concern Submissions", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Office of Refugee Resettlement (ORR) has amassed a large backlog of Notice of Concern (NOC) PDF forms that contain critical information regarding safety of children who have left ORR's care. The AI-powered triage tool will support Office of Refugee Resettlement staff more effectively and efficiently review NOCs by helping structure and prioritize received NOCs. Through more effective triage, ORR will begin to cut down on the large backlog that has accumulated." }, { "tracker_id": "hhs-2024-99cd7b5399", "fiscal_year": "FY2024", "use_case_name": "Unaccompanied Children Program Policy & Procedure Research Tool", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Office of Refugee Resettlement (ORR) conducts monitoring visits at least monthly to ensure that care providers meet minimum standards for the care and timely release of unaccompanied children, and that they abide by all Federal and State laws and regulations, licensing and accreditation standards, ORR policies and procedures, and child welfare standards. If ORR monitoring finds a care provider to be out of compliance with requirements, ORR issues corrective action findings and requires the care provider to resolve the issue within a specified time frame. Compliance determination involves research into the various laws, standards, policies, and procedures. The UC Program Policy & Research Tool assists the UC monitoring team with research that in many cases take a long time. The goal of the UC Program Policy & Research Tool is to speed up this research as children's health and well-being may be impacted before a corrective action finding is issued and the issue is resolved." }, { "tracker_id": "hhs-2024-667bf33d64", "fiscal_year": "FY2024", "use_case_name": "Unaccompanied Children Process Model Digital Twins", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Office of Refugee Resettlement (ORR) Unaccompanied Children Bureau provides a safe and appropriate environment to children who enter the United States without immigration status and without a parent or legal guardian who is able to provide for their physical and mental well-being (referred to as “unaccompanied children”). ORR funds residential care provider facilities that provide temporary housing and other services to unaccompanied children in ORR custody. ORR and its care providers work to ensure that children are released timely and safely from ORR custody to parents, other family members, or other adults (often referred to as “sponsors”) who can care for the child’s physical and mental well-being. Unaccompanied children remain in ORR’s care and custody until they are released to a parent or other sponsor in the United States, are repatriated to their home country, obtain legal status, or turn 18 years old, at which time they are transferred to the custody of DHS. There are many processes involved throughout. The goal is for ORR to be more proactive in strategic planning and contingency planning." }, { "tracker_id": "hhs-2024-beb9e6e418", "fiscal_year": "FY2024", "use_case_name": "Analyzing Public Comments on Proposed Rule", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "In the federal rulemaking process, the public has an opportunity to provide comments on proposed new regulation. AI was used to support the public comment review process for a proposed rule to make quality improvements in the Head Start program. The public comment analysis pipeline provided an Excel spreadsheet of comment segments that were tagged and Word files of topic-based summary drafts. Subject matter experts from the Office of Head Start reviewed comments by topic and further analyzed and refined the topic-based summaries. The benefit of using AI for this purpose is improved baseline information following public comment to support more expeditious final rule development." }, { "tracker_id": "hhs-2024-c90a6e2158", "fiscal_year": "FY2024", "use_case_name": "Outreach List Segmentation", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Office of External Affairs (OEA) is responsible for sharing information from ACF with external constituents. OEA had a long list of constituent email addresses that were without context. They wanted the ability to send targeted emails to different groups of people whose organizations work in particular policy spaces. The AI tool returned a spreadsheet of the provided domains with structured information on likely organization name, summary of their mission, policy domains (e.g. public health, education), and website. This spreadsheet allows OEA to segment their lists and target organizations more efficiently." }, { "tracker_id": "hhs-2024-1f99892965", "fiscal_year": "FY2024", "use_case_name": "Ask HR Policy", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This project provides a resource for ACF Executive Officers/Administrative Officers and managers to easily navigate Human Resources (HR) Policy documentation and quickly get answers to their questions that are based on the official HR Policy Library that is on the HHS.gov website (https://www.hhs.gov/about/agencies/asa/ohr/hr-library/index.html) Ask HR Policy provides a secure interface permissioned only to select ACF Executive Officers and Administrative Officers. Users type in questions about managing employees covered by the HR Policy Library, and Ask HR Policy provides a narrative of its “thought process” then suggests an answer based in the documentation, alongside links that take the user to the relevant section of the official document." }, { "tracker_id": "hhs-2024-b44af0d7ee", "fiscal_year": "FY2024", "use_case_name": "Child Welfare Information Gateway OneReach Application", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Childrens Bureau runs the Children Welfare Information Gateway, a connection to trusted resources on the child welfare continuum. The Information Gateway has a hotline for answering questions or requesting information: https://www.childwelfare.gov/stay-connected/contact/ The Information Gateway hotline automates some of the more routine questions and requests, allowing staff to focus on more complex, nuanced situations." }, { "tracker_id": "hhs-2024-0538ede6f5", "fiscal_year": "FY2024", "use_case_name": "Collective Bargaining Compass", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This project provides a resource for ACF managers to easily navigate Collective Bargaining Agreement documentation and quickly get answers to their questions that are based on the official 400-page rulebook. The Collective Bargaining Compass provides a secure Virtual Assistant interface permissioned only to select ACF managers. Users type in questions about managing employees covered by the Collective Bargaining Agreement, and the Virtual Assistant provides a narrative of its “thought process” then suggests an answer based in the documentation, alongside links that take the user to the relevant section of the official document." }, { "tracker_id": "hhs-2024-2c1047ebb5", "fiscal_year": "FY2024", "use_case_name": "Discover User Assistant", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This project provides a resource for ACF Discover Staff Management Module users (i.e. ACF Executive Officers and other ACF administrative and management staff) to easily navigate ACF Discover Staff Management platform and instructional documentation and quickly get answers to their questions about the module’s features. The User Documentation Assistant provides a secure virtual assistant interface that is only available to ACF Discover Users. Users are able to ask the assistant specific questions about the capabilities of ACF Discover along with how to leverage tools and applications. The Virtual Assistant is able to provide answers by referencing the User Reference guide." }, { "tracker_id": "hhs-2024-060e1e7033", "fiscal_year": "FY2024", "use_case_name": "Policy Knowledge Base Data Migration", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Office of Family Assistance (OFA) provides technical assistance to grantees, including answering questions about policy regarding the Temporary Assistance for Needy Families (TANF) program. Historically, OFA has tracked its accumulated TANF knowledge across disparate locations and file types. Given the quantity of knowledge base articles involved in this project (>1,100), the AI draft article topics and categories helped speed up the creation of the knowledge base minimum viable product." }, { "tracker_id": "hhs-2024-c44fd7567b", "fiscal_year": "FY2024", "use_case_name": "Qualitative analysis", "previous_name_reported": "", "bureau_as_reported": "ACF", "component": "ACF", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "ACF staff often conduct surveys and interviews, which generate qualitative data that needs to be analyzed for themes and trends. The AI functionality assists users in identifying relevant passages in their narrative data related to specific topics." }, { "tracker_id": "hhs-2024-1efef14436", "fiscal_year": "FY2024", "use_case_name": "AHRQ Search", "previous_name_reported": "", "bureau_as_reported": "AHRQ", "component": "AHRQ", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Organization wide search that includes Relevancy Tailoring, Auto-generation Synonyms, Automated Suggestions, Suggested Related Content, Auto Tagging, and \"Did you mean?\" to allow visitors to find specific content. This AI use case enhances our agency's efficiency and user experience by optimizing search results, auto-completing queries, suggesting relevant searches and content tags, as well as proposing spelling corrections." }, { "tracker_id": "hhs-2024-edbb4ebb9b", "fiscal_year": "FY2024", "use_case_name": "Chatbot", "previous_name_reported": "", "bureau_as_reported": "AHRQ", "component": "AHRQ", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This use case introduces a conversational interface that enables users to ask questions about AHRQ content using natural language. This will serve as a modern, efficient replacement for the traditional public inquiry telephone line. Improved user experience and reduced resources responding to public inquiries" }, { "tracker_id": "hhs-2024-a60f4354fc", "fiscal_year": "FY2024", "use_case_name": "Chatbot", "previous_name_reported": "", "bureau_as_reported": "AHRQ", "component": "AHRQ", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "For the Patient Safety Organization Privacy Protection Center website, this use case provides an interface that allows users to conversationally ask questions about content in natural language. Improved user experience and reduced resources responding to public inquiries" }, { "tracker_id": "hhs-2024-764d97016d", "fiscal_year": "FY2024", "use_case_name": "Enhancing Diversity in Peer Review - Pilot", "previous_name_reported": "", "bureau_as_reported": "AHRQ", "component": "AHRQ", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Division of Scientific Review (DSR) is aiming to enhance diversity in AHRQ peer review groups by revising recruitment strategies, aiming for 20% representation from underrepresented minorities in each study section. This process, involving networking, outreach, and various resources, typically takes at least three months. By utilizing AI to swiftly and accurately identify diverse and qualified grant reviewers, our agency will foster a more efficient process for identifying scientific reviewers and a more equitable and inclusive scientific review process. This will help us achieve our goal of providing safer, high-quality, accessible, and affordable healthcare to individuals and communities throughout the U.S., thereby improving the overall effectiveness of our agency." }, { "tracker_id": "hhs-2024-6fe91ab41b", "fiscal_year": "FY2024", "use_case_name": "NLQuery- As Data or Pulse", "previous_name_reported": "", "bureau_as_reported": "AHRQ", "component": "AHRQ", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "As a feature to clinical dashboards available on a secured page on the Patient Safety Organization Privacy Protection Center website for PSOs who submit data, provides capability to allow user to type a question in common language and instantly get a response. Answers come in the form of automatic data visualizations, with no need to manually drag-and-drop fields or understand the nuances of user's data’s structure. Improved functionality of the clinical dashboards. Potential to encourage additional reporting of data in light of benefit to user." }, { "tracker_id": "hhs-2024-6ba2c62cd0", "fiscal_year": "FY2024", "use_case_name": "Quality and Safety Review System AI-enabled automated abstraction", "previous_name_reported": "", "bureau_as_reported": "AHRQ", "component": "AHRQ", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The aim of this AI use case is to enhance or eliminate the need for manual abstraction of patient records and manual data entry into the QSRS tool, thereby streamlining the data entry process. This AI use case has the potential to significantly help by increasing the volume of cases abstracted while maintaining or lowering government expenditure, thereby enabling more work to be done at the same or reduced cost. Additionally, it can maintain the number of cases abstracted with improved efficiency, thus also reducing government costs by achieving the same output at a lower cost. Furthermore, it will improve abstraction accuracy and expand AI/ML capabilities at both the national and local hospital levels while ensuring data quality improvements so there is trusted and reliable data for reporting purposes." }, { "tracker_id": "hhs-2024-1b2d064993", "fiscal_year": "FY2024", "use_case_name": "Document Q&A Interface", "previous_name_reported": "", "bureau_as_reported": "ASPR", "component": "ASPR", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Law & Justice", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The SCCT consolidates a number of documents for every response that it monitors, and often analysts have questions about response context stored in those documents Increase an analyst / user on Protect's knowledge of a specific response or MCM by allowing them to \"ask questions\" of associated / uploaded SCCT media" }, { "tracker_id": "hhs-2024-04db4d08b6", "fiscal_year": "FY2024", "use_case_name": "Emergency Inventory Redistribution Model", "previous_name_reported": "", "bureau_as_reported": "ASPR", "component": "ASPR", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Emergency Management", "ai_type_as_reported": "Not reported", "public_health_functions": [ "emergency" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Optimization models are needed to identify efficient preparedness and responses strategies for inventory management and distribution during an emergency. This can be used to identify possible interventions." }, { "tracker_id": "hhs-2024-205d282b2f", "fiscal_year": "FY2024", "use_case_name": "emPOWER AI", "previous_name_reported": "", "bureau_as_reported": "ASPR", "component": "ASPR", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "emergency" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Public health authorities, first responders and others across the emergency management spectrum indicated that they had to be able to more rapidly access emPOWER publicly available data, particularly in unstable internet conditions, during disasters. emPOWER AI, an Amazon Alexa Skill, was created to allow anyone with a smartphone to be able to request the data and informational resources. First responders, public health authorities, emergency managers, health care providers, and other community partners can use emPOWER AI prior to, during, and after an incident, emergency, or disaster to anticipate, plan for, and respond to the access and functional needs of at-risk populations in their communities using publicly available emPOWER data, informational resources and training." }, { "tracker_id": "hhs-2024-cb45722a74", "fiscal_year": "FY2024", "use_case_name": "Entity Resolution Tool", "previous_name_reported": "", "bureau_as_reported": "ASPR", "component": "ASPR", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "SCCT receives order information that references thousands of manufacturers and customers. These data need to be standardized. This allows analysts to aggregate data by manufacturer or customer to better characterize order and delivery patterns." }, { "tracker_id": "hhs-2024-0f62ba8729", "fiscal_year": "FY2024", "use_case_name": "Executive Report Generation AI Copilot", "previous_name_reported": "", "bureau_as_reported": "ASPR", "component": "ASPR", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "SCCT produces executive summaries and commercial product awareness reports (CPARs) on supply chain disruptions, weather events, shortages, etc. These utilize a large amount of SCCT data that needs to be summarized and explained in detail. Used to decrease the amount of time needed to produce an executive report, reducing the need for SCCT analysts to create them from scratch every time" }, { "tracker_id": "hhs-2024-5291a66e8f", "fiscal_year": "FY2024", "use_case_name": "Flow Model Edge Prioritization Estimation Tool", "previous_name_reported": "", "bureau_as_reported": "ASPR", "component": "ASPR", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Emergency Management", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This model is needed to investigate potential impacts of supply disruptions. This is one component in a simulation tool used to quantify the impact of supply chain disruptions on customers." }, { "tracker_id": "hhs-2024-a3d75710b2", "fiscal_year": "FY2024", "use_case_name": "Generalized Demand Model Sensitivity Analyzer", "previous_name_reported": "", "bureau_as_reported": "ASPR", "component": "ASPR", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The generalized demand model is used by SCCT to create demand projections for critical medical products. This is a diagnostic tool used to help analysts understand and refine their models." }, { "tracker_id": "hhs-2024-d3c591fb89", "fiscal_year": "FY2024", "use_case_name": "Google Search Model Alerts", "previous_name_reported": "", "bureau_as_reported": "ASPR", "component": "ASPR", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Publicly available information can enhance the ability to identify supply chain disruptions. This provides automated alerts that can help trigger review of the relevant data by analysts." }, { "tracker_id": "hhs-2024-96507a004e", "fiscal_year": "FY2024", "use_case_name": "Google Search Model Rising Trend Entity Extraction", "previous_name_reported": "", "bureau_as_reported": "ASPR", "component": "ASPR", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Publicly available information can enhance the ability to identify supply chain disruptions. The list of products produced by this model helps analysts identify products that may be in shortage." }, { "tracker_id": "hhs-2024-24491f094b", "fiscal_year": "FY2024", "use_case_name": "Manufacturer Name Standardization", "previous_name_reported": "", "bureau_as_reported": "ASPR", "component": "ASPR", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The SCCT receives many manufacturer name variants from its many distributors that need to be consolidated and standardized This allows analysts to have more standardized manufacturer names for reporting" }, { "tracker_id": "hhs-2024-b01f4ddb98", "fiscal_year": "FY2024", "use_case_name": "Model based open source reporting search and summarization", "previous_name_reported": "", "bureau_as_reported": "ASPR", "component": "ASPR", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Law & Justice", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "SCCT collects open source information for monitoring supply chain events. Language models provide an initial summary that analysts can use to select topics of interest in order to focus on relevant events." }, { "tracker_id": "hhs-2024-e2314ec7ab", "fiscal_year": "FY2024", "use_case_name": "PPE Product Classification Model", "previous_name_reported": "", "bureau_as_reported": "ASPR", "component": "ASPR", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "SCCT receives information about thousands of products from distributors. This is used to predict if the product falls into one of the following 7 categories: 1) Gloves – Surgical & Exam, 2) Needles & Syringes, 3) Gowns – Isolation & Surgical, 4) Masks – N95, 5) Masks – Surgical/Procedural, 6) Eye / Face Shields, 7) Test Kits. These classifications are used to aggregate data in order to make it possible to analyze trends." }, { "tracker_id": "hhs-2024-ff787ea5ae", "fiscal_year": "FY2024", "use_case_name": "Adverse Childhood Experiences (ACEs) Literature Review Dashboard", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This solution will impact the process of conducting literature reviews for scientific research related to ACEs. Identifying relevant articles in a literature review is a time intensive process. Utilizing large language models, this system enables quick identification of relevant articles to be included in a ACEs literature database. This database allows ACEs researchers within CDC's National Center for Injury Prevention and Control a quick reference to develop hypotheses and identify gaps in the ACEs literature" }, { "tracker_id": "hhs-2024-f79f1d2101", "fiscal_year": "FY2024", "use_case_name": "Automated Analysis of Injury Control Research Center (ICRC) Annual Progress Reports (APRs) using Large Language Models", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This AI affects the process of reviewing and analyzing the Annual Progress Reports (APRs) submitted by Injury Control Research Centers (ICRCs). Specifically, the AI will focus on identifying and analyzing text sections of the APRs that detail reported challenges, as well as other text-heavy sections in future stages of the project. The AI is designed to streamline the review process, improve efficiency, and support the evaluation of the performance and progress of ICRC-funded activities. The output of the AI will help to quickly and efficiently identify key challenges and insights from ICRC APRs, enabling more effective decision-making in the review process. By automating the extraction and analysis of critical information, the AI allows the ICRC team to focus on higher-level evaluation and strategic planning. The AI will help reduce the time and resources needed for manual review, improve the consistency and accuracy of assessments, and facilitate faster responses to ICRC needs. Ultimately, this will support ICRCs in overcoming challenges and achieving their research and injury control goals, benefiting the public health system as a whole." }, { "tracker_id": "hhs-2024-9201813c2e", "fiscal_year": "FY2024", "use_case_name": "Detecting Stimulant and Opioid Misuse and Illicit Use", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Federal Health Survey analysis. The machine learning models created in this project will have an impact on the statistical analysis of electronic health record (EHR) data gathered through the (NHCS) National Hospital Care Survey. Furthermore, when the model is made available to the public, it may also influence the analysis of other user datasets that contain EHR clinical notes. Certain non-therapeutic models do not have corresponding medical codes available in clinical notes and the information is only available in free-text fields. The machine learning models help to discover non-therapeutic drug use as described in EHR clinical notes which are novel insights previously impossible to gleam from EHRs without AI." }, { "tracker_id": "hhs-2024-54d6b0e3ef", "fiscal_year": "FY2024", "use_case_name": "DHP Virtual Assistant", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Literature Review for HIV research. The AI virtual assistant will help HIV researchers with information retrieval on information related to HIV and HIV research. Our AI virtual assistant will help increase productivity among our HIV researchers by retrieving information related to HIV and HIV research." }, { "tracker_id": "hhs-2024-a89710c963", "fiscal_year": "FY2024", "use_case_name": "Fuzzy matching tool", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Clearance and review management of CDC publication. CDC's Office of Science (OS) manages the eClearance information system, which is responsible for tracking the submission of CDC-authored journal manuscripts and Morbidity and Mortality Weekly Reports (MMWRs) for CDC's internal peer review process. This tool will help ensure CDC-authored publications have completed the National Institutes of Health Manuscript Submission (NIHMS) process and are compliant with CDC's public access to publications policy. It will also be used to help centers and divisions identify publications from their organization for science prioritization and impact analyses." }, { "tracker_id": "hhs-2024-8a80cda25a", "fiscal_year": "FY2024", "use_case_name": "Global (GIB) Initiative for Influenza Vaccine Equity (GIIVE): Ensuring Universal Access", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Literature Review The output is a list of abstracts that may have related information to vaccine inequity. These identified abstracts will be reviewed \"by-hand\" and then those associated publications will be reviewed by epidemiologists further." }, { "tracker_id": "hhs-2024-3621efe43d", "fiscal_year": "FY2024", "use_case_name": "HIV Data Virtual Assistant", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The AI will impact the data retrieval and automation process for HIV researchers, specifically in providing information from datasets and potentially generating code for desired variable names and analysis or generating information about a particular dataset. This solution aims to alleviate the challenges faced by scientists searching for the appropriate HIV-related data to address their inquiries. This will improve productivity and research efforts." }, { "tracker_id": "hhs-2024-343605175f", "fiscal_year": "FY2024", "use_case_name": "Identify infrastructure supports for physical activity (e.g. sidewalks) in satellite and roadway images", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "The physical environment, such as sidewalk availability, in areas may be correlated to levels of physical activity. The Physical Activity (PA) and Health Branch is responsible for monitoring and gathering data on physical activity behaviors, as well as assessing the environments that promote physical activity. The implementation of this technology has the potential to streamline the surveillance of sidewalks, which play a crucial role in supporting physical activity (PA). The existing methods for cataloging sidewalks, such as manual inspection, are labor-intensive and costly. However, this technology has the capability to significantly minimize the effort required for such tasks." }, { "tracker_id": "hhs-2024-5e42b80200", "fiscal_year": "FY2024", "use_case_name": "Immunization Information Systems Guidance Documentation Navigation and Management (IDAB EDAV Azure OpenAI Technology Use)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This AI solution (chatbot) helps with retrieving information and updating guidance documentation by increasing interactivity and discoverability of information in the Immunization Information Systems (IIS) domains of HL7 electronic data exchange standards, CDSi (Clinical decision support for immunizations), and IIS operational best practices. It provides a modernized approach to knowledge retrieval and real-time guidance, as well as support the creation of new guidance by leveraging the AI capabilities in natural language processing and content generation to draft, refine, and validate new guidance documents efficiently and accurately. This AI solution creates a better, faster, and more actionable method for IIS SMEs to obtain IIS guidance from previously published and publicly available IIS documentation. This solution will provide a more modernized approach to knowledge retrieval and real-time guidance, as well as support the creation of new guidance by leveraging the chatbots capabilities in natural language processing and content generation to draft, refine, and validate new guidance documents efficiently and accurately. This AI solution (chatbot) provides an efficient and user-friendly way to search, interact, and update these IIS guidance documents. It can help experts answer questions faster, new employees find information more easily, and employees better understand best practices." }, { "tracker_id": "hhs-2024-f2c316230a", "fiscal_year": "FY2024", "use_case_name": "LaserAI", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This project aims to evaluate a commercial off the shelf, software as a service systematic review project management software. This software uses machine learning/ natural language processing to support title and abstract screening, PDF retrieval, full text review, and most importantly data extraction to reduce screening time and improve accuracy of all phases of a systematic review. The extracted data will be synthesized and graded, and will be used to inform the development of evidence-based infection prevention and control recommendations for healthcare settings." }, { "tracker_id": "hhs-2024-5264aae6ff", "fiscal_year": "FY2024", "use_case_name": "NewsScape", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Development of early warning indicators. News articles can form an early warning indicator of public health events and other pieces of information which can be utilized across all major domains of public health. Because of the quantity of news articles, manual efforts are impossible to gather this information. There are a variety of endpoints that this AI output could help support. Various teams across the CDC have expressed interest in being able to quickly get the right news content, with summaries of those articles to help support outbreak detection, report generation, surveillance and monitoring of pathogen specific news, etc. AI lets us efficiently filter and summarize thousands of news articles a day into a handful of daily \"news events\" that user can glean information from." }, { "tracker_id": "hhs-2024-fb5bef0b48", "fiscal_year": "FY2024", "use_case_name": "RAPID Analysis of Policy and Program Documents (RAPID)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Policy and documentation awareness and evaluation The web application will: 1) save staff time by streamlining laborious document reviews; 2) expand capacity by reducing the need for specialized staff training and skills; 3) provide complete, plain language answers to policy surveillance and evaluation questions along with binary codes or scores; 4) answer questions consistently (i.e., reduce intra-rater variability for redundant coding); and 5) enable easy validation of AI-provided answers." }, { "tracker_id": "hhs-2024-b7565f5d42", "fiscal_year": "FY2024", "use_case_name": "SewerScout: Automated on-site sewage facility detection from aerial imagery to identify failed systems", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance", "environmental-occupational" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "This business process focuses on the automation of the accurate identification of onsite wastewater systems in state, tribal, local, and territorial (STLT) health jurisdictions. This application scans aerial imagery and uses object detection and image classification models to detect onsite sewage facilities. Initial work is focusing on the ability to identify known systems. The intent of this project is to identify failed systems, which can be sources of soil and drinking water contamination as well as vectors that carry or transmit disease in the community. This project will allow STLT partners to readily characterize the location and functional status of septic systems without the time and resource consuming effort of visiting each household. The resources in many local health districts (LHDs) are barely adequate to undertake assessments of suitability for new systems (if not entirely contracted out) and most LHDs do not have any program to routinely inspect current systems. If successful, this project will allow state, tribal, local, and territorial public health departments to more easily identify failing septic systems and work to address them. If successful, subsequent work will work on differentiating functional from failed systems. Identification of septic systems during routine operations would help bolster and expedite disaster response efforts after major disasters by having a ready catalog of systems and using pre- / post-disaster satellite images to direct resources in a much more efficient way than on-the-ground door-to-door surveys in rural / remote locations." }, { "tracker_id": "hhs-2024-1c1895cfa1", "fiscal_year": "FY2024", "use_case_name": "Sidekick Comms bot Offering User-friendly Tips (SCOUT)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The AI will impact the content creation processes for CDC.GOV and reduce burden in creating new webpages. CDC sometimes uses GenAI (generative artificial intelligence) to help create content for the public. This may include content for the web, social media, fact sheets, and graphics. In every case, our experts review our communication materials in an effort to ensure accuracy and quality before use. CDC public health research, data, and recommendations are based on the best science currently available and go through a separate agency review process prior to release. The solution aims to reduce the workload of health communicators by simplifying the process of creating new web pages and social media posts. By leveraging Generative AI technology, we can produce new content much more quickly than if communications staff were to do it on their own. The goal is to make the published content more accessible and understandable for the general public, ensuring that CDC information reaches a wider audience. It's important to note that all content generated by the AI is carefully reviewed and edited by a human before it is used and the follow disclaimer is stated with public content that this tool has been used with: \"CDC sometimes uses GenAI (generative artificial intelligence) to help create content for the public. This may include content for the web, social media, fact sheets, and graphics. In every case, our experts review our communication materials in an effort to ensure accuracy and quality before use. CDC public health research, data, and recommendations are based on the best science currently available and go through a separate agency review process prior to release.\"" }, { "tracker_id": "hhs-2024-6a17a9a204", "fiscal_year": "FY2024", "use_case_name": "Transcribing Cognitive Interviews with Whisper", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Federal Health Survey Research. Specifically, Cognitive interviews are a form of qualitative research where interviewers ask probing questions to understand how the interviewee understands a concept which is being asked about in a federal survey. These interviews are a key part of survey design to identify any potential misconceptions and prevent lengthy and hard to answer questions which could create potentially wrong health statistics. These interviews are time consuming to perform and report as they are inherently individual and require extensive hours to transcribe. By using AI to transcribe the interviews, researchers can potentially cut down on the hours or qualitative review, increasing the speed of research publication and quality of survey questions used on federal surveys. Without transcripts, these interviews have to be listened to repeatedly taking at least the same amount of time as the full length of the interview to find important pieces of information for their work. These transcripts can greatly reduce the burden on researchers by enabling immediate comparison of concepts and answers along with timestamps created for researchers to reference in review." }, { "tracker_id": "hhs-2024-22683e09d8", "fiscal_year": "FY2024", "use_case_name": "Use of Natural Language Processing for Topic Modeling to Automate Review of Public Comments to Notice of Proposed Rulemaking", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This use case is aimed to assist in the review of public comments for Notices of Proposed Rulemaking. All responses are manually reviewed following this tool usage. By law, Public comments are required to be manually review for any notice of public rule making. These comments are highly labor intensive due to both the quantity and wide range of potential opinions shared by the public. These comments can range in both length of response and quantity serving as a challenge for properly ensuring the insights from the public are accurately gathered in the review process. By using an LLM based Topic Modeling approach, manual reviewers can organize responses based upon theme to enhance both the insights gained, the speed of manual review, and ensure all topics mentioned can be accurately reported in all review processes. This process can use open source python libraries and open-source LLMs to complete different actions. This AI methodology enables manual review processes to be more effective in gaining insights from the public, better organized in themes and categories present within text, and reduce the burden of manual review of comments from notices of proposed rulemaking required by law." }, { "tracker_id": "hhs-2024-d2ae413658", "fiscal_year": "FY2024", "use_case_name": "Center for Forecasting and Outbreak Analytics Disease Modeling", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance", "forecasting", "emergency" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Federal and State, Tribal, Local, and Territorial health department responses to disease outbreaks depends on being able to predict the future behavior of a given outbreak as well as the potential impact of different responses to the outbreak. This project aims to build models of disease spread that allow for (1) determining if and where infection/impact rates are growing or increasing (2) forecasting future disease progression (3) modeling specific scenarios such as the impact of vaccination. We use model output to provide information to Federal and SLTT decision makers to improve disease response. This allows those decision makers to determine where and how to direct resources in a disease out break as well as the impact of potential response actions. This kind of insight is critically dependent on mathematical modeling." }, { "tracker_id": "hhs-2024-c1bfcedd12", "fiscal_year": "FY2024", "use_case_name": "ChatCDC- CDC Enterprise GenAI Chatbot: Bring your own Enterprise Data (Data Analysis)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This AI system is an extension of the GenAI chatbot but is only available to a small group of pilot testers as of November 5, 2024. It operates through an ATO'ed instance of Microsoft Azure OpenAI through CDC's Enterprise Data Analytics and Visualization Platform. Using a method called retrieval augmented generation (RAG), this enhancement enables staff to augment the existing GenAI chatbot with access and “knowledge” of internal or enterprise data such as documents, spreadsheets, and PDFs. Additionally, each chatbot response has citations referencing to the sections of source text that informed the response, which facilitates human-in-the-loop validation for accuracy and relevancy of AI-generated content. One way users are using this tool is for data extraction, which accelerates analysis of large document sets. By extracting key points from large volumes of text in various file formats, the enterprise-data-enhanced chatbot streamlines access to critical insights from reports, internal documentation, publications, and other documents. For example, one team is using the chatbot to support literature review by extracting information related to housing status and HIV in the US from a set of pre-screened publications. Examples of this information include a binary yes/no on whether the research paper focuses on the topic of interest, the paper’s population of focus, and the type of paper. The enterprise data chatbot’s ability to quickly and accurately extract relevant information reduces manual search time and errors associated with human data retrieval. The output will be leveraged for more accurate data analysis, timely insights, and better-informed decision-making processes." }, { "tracker_id": "hhs-2024-aee2306e93", "fiscal_year": "FY2024", "use_case_name": "ChatCDC- CDC Enterprise GenAI Chatbot: Bring your own Enterprise Data (Summarization)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This AI system is an extension of the GenAI chatbot but is only available to a small group of pilot testers as of November 5, 2024. It operates through an ATO'ed instance of Microsoft Azure OpenAI through CDC's Enterprise Data Analytics and Visualization Platform. Using a method called retrieval augmented generation (RAG), this enhancement enables staff to augment the existing GenAI chatbot with access and “knowledge” of internal or enterprise data such as documents, spreadsheets, and PDFs. Additionally, each chatbot response has citations referencing to the sections of source text that informed the response, which facilitates human-in-the-loop validation for accuracy and relevancy of AI-generated content. The enterprise data chatbot’s summaries will enhance operational, informational, and decision-making processes that would otherwise require extensive research and reading time. For an example of a more informational use case, one team provided the chatbot with documentation and resources about a division’s organizational structure, projects, administrative processes, and tools to support the orientation of new staff and (re)orientation of existing staff. The chatbot enables the division’s staff to efficiently find resources for their roles; better understand the division’s initiatives; and track the activities involved in the onboarding process for new staff." }, { "tracker_id": "hhs-2024-7332b020f8", "fiscal_year": "FY2024", "use_case_name": "Leveraging AI for Metadata Tagging for Enterprise Data Catalog of CDC", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "CDC’s Enterprise Data Catalog provides an internal single centralized inventory of available datasets within CDC staff. It provides data management and search tools to help users find what they need and aims to provide descriptions to aid in relevance determinations for users. This is from the appropriate metadata, e.g. project title, description, or other important information describing the dataset. Because of the previous manual metadata tagging process, many datasets catalogued do not have the appropriate tags to help staff determine the relevance of any particular dataset available within CDC for particular tasks. Because of the lack of standardization and gaps in metadata fields, this AI's output will greatly increase the speed for applying the appropriate metadata fields for each dataset within the catalogue to increase the usability of the product. This reduces the time and effort required for manual tagging and improves the consistency and completeness of metadata." }, { "tracker_id": "hhs-2024-9f56383050", "fiscal_year": "FY2024", "use_case_name": "Malaria parasites DNA barcode geography classification", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Epidemiological investigations of Malaria To complement epidemiologic investigations of domestic malaria cases, including cases that were domestically acquired. This information can help assess the origin of the malaria strain in question, which can help us understand how the strain entered the US. For more information, please see the following manuscript: https://journals.asm.org/doi/full/10.1128/aac.01203-24" }, { "tracker_id": "hhs-2024-83ba30b797", "fiscal_year": "FY2024", "use_case_name": "The School Closure Awareness System", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Emergency Management", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Identifying unplanned school closures. CDC is responsible for supporting schools before, during, and after and emergency. During COVID 19 this included monitoring school closure or virtual status and reporting this to federal partners, the White House, and the public. Due to cost the process used during COVID was no longer feasible and ceased in DEC 2022. We explored multiple ways to capture this data and our current system met the need best. This AI has allowed us to save nearly 2-million-dollars from contracting fees and decrease human work hours by 200 hours. It has made the capture of some of this data possible after cessation of the process used during COVID 19 which we could no longer support. The AI supported system is faster and captures more information about individual closures than the original human-powered process." }, { "tracker_id": "hhs-2024-e77aa02882", "fiscal_year": "FY2024", "use_case_name": "Using Generative AI for Stance Analysis of Public Comments on CDC’s Proposed Rules", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "By law, Public comments are required to be manually review for any notice of public rule making. These comments are highly labor intensive due to both the quantity and wide range of potential opinions shared by the public. These comments can range in both length of response and quantity serving as a challenge for properly ensuring the insights from the public are accurately gathered in the review process. By using an LLM based Topic Modeling approach, manual reviewers can organize responses based upon theme to enhance both the insights gained, the speed of manual review, and ensure all topics mentioned can be accurately reported in all review processes. This process can use open source python libraries and open-source LLMs to complete different actions. As public feedback on various rules gets collected across the CDC’s divisions, this initiative along with other NLP approaches can potentially save time for CDC’s public policy experts during public comment review." }, { "tracker_id": "hhs-2024-f73eec4269", "fiscal_year": "FY2024", "use_case_name": "A reusable NLP pipeline for clinical narratives preprocessing and characterization", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Data cleaning of Clinical narratives from health records. At scale, clinical narratives are a dataset available to CDC currently underutilized as there is not a structured pipeline to identify structured information output. We are currently working on setting up a process to create a pipeline that cleans data markup (html, rtf, xml) and identifies signs and symptoms using pre-trained Named Entity Recognition (NER) tasks from relevant models published in peer review journals. The use of these pretrained models significantly reduces the amount of time a human to spend tagging data and provides additional features and increased consistency from unstructured data for identification of more novel insights." }, { "tracker_id": "hhs-2024-b3464f99e6", "fiscal_year": "FY2024", "use_case_name": "Autocoding to Support Adverse Drug Event Surveillance", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance", "product-safety" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "The model will help surveillance epidemiologists working at CDC to determine whether reported potential adverse drug events in the National Electronic Injury Surveillance System Cooperative Adverse Drug event Surveillance meet the agency's surveillance case definition for the same. It will speed up coding of the drug event reports, helping epidemiologists produce prevalence estimates for the surveillance system." }, { "tracker_id": "hhs-2024-f3eecc5674", "fiscal_year": "FY2024", "use_case_name": "Automating LIMS Bioinformatics Workflow Configuration and Enhancing Lab Quality Management with AI", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "The tool proposed will impact the laboratory bioinformatics workflow configuration within the Core Laboratory's Genomics Sequencing Lab (GSL). Specifically, it will improve the efficacy with Clarity LIMS for tracking and processing Next Generation Sequencing (NGS) samples. This includes the creation and customization of bioinformatics workflows in the Laboratory Information Management System, which is currently a time-consuming and expertise-intensive task for bioinformaticians and laboratory scientists. The RAG system can potentially reduce the time required to configure Clarity LIMS workflows, enabling rapid deployment which is particularly crucial during pandemics or outbreak responses. By simplifying the workflow customization process through natural language inputs, it lowers the barrier to entry for laboratory scientists and bioinformaticians who may not be familiar with XML scripting. The system also enhances team learning and training by providing easy access to relevant documents on quality management and regulations, ensuring that all team members are well-informed about best practices." }, { "tracker_id": "hhs-2024-8282785e64", "fiscal_year": "FY2024", "use_case_name": "DGMH AI Chatbot", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The DGMH AI chatbot would assist with developing an initial draft response to inquiries where the response could have been found on our website. This would allow DGMH staff to have a starting point on a response to edit and clear. Additionally, the response could be adjusted for plain language to assist in ensuring the inquirer understands the response being sent back to them. The AI chatbot would allow inquiries to be responded to quicker and would allow staff to focus on other high priority items as well. The project will rely on using Retrieval-Augmentation Generation (RAG) to identify relevant information from CDC’s relevant website pages to generate relevant responses using AI. Evaluation of the project will assess how accurate the response was, how complete it was, and how much revision was needed. Evaluation would also explore the answer and if the answer that was generated was consistent throughout time if multiple people asked similar questions. The goal is consistency of content rather than tone/plain language. The chatbot will help to reduce the turnaround time for responding to inquiries and free up time for staff to address other priorities." }, { "tracker_id": "hhs-2024-201843388c", "fiscal_year": "FY2024", "use_case_name": "Distiller SR: AI to screen research articles for Community Guide reviews", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This tool will be used to screen research articles that will be used to inform Community Preventive Services Task Force recommendations (a body that produces guidelines independent of CDC). Use of this technology may increase the speed with which systematic reviews can be conducted. This may expedite the time it takes to evaluate the effectiveness of public health programs for a CPSTF recommendation (not a CDC guideline)." }, { "tracker_id": "hhs-2024-34e55878f8", "fiscal_year": "FY2024", "use_case_name": "Evaluating Generative AI for polio containment.", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "laboratory" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "The AI will impact the data retrieval and automation process for HIV researchers, specifically in providing information from datasets and potentially generating code for desired variable names and analysis or generating information about a particular dataset. As polio eradication progresses, containment of poliovirus becomes critical. This project aims to identify and evaluate Generative AI tools to extract information from publications in an effort to identify poliovirus in places previously thought to be polio-free. Identification of such a tool may diversify Polio and Picornavirus Branch's surveillance portfolio and increase Branch's capability of processing large volume of data for containment purposes. This project may support CDC PPLB’s polio surveillance activity and containment efforts." }, { "tracker_id": "hhs-2024-898339af9a", "fiscal_year": "FY2024", "use_case_name": "Respiratory Virus Response (RVR) Data Analysis Concept", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This solution will impact the process of summarizing subject matter expert (SME) interpretations/knowledge during a response. Response efforts require prompt turnaround of essential information to the public. This project will impact public health by improving the efficiency of dissemination of information through production of key takeaways which can then be quickly reviewed by SMEs and cleared. This project will also allow for improved understanding of limitations and uncertainties in use of AI methods that could contribute to improved implementation of such methods in public health." }, { "tracker_id": "hhs-2024-8975f573c6", "fiscal_year": "FY2024", "use_case_name": "School LLM initial abstract review process", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Reviewing research related to school readiness science. CDC/DRRS' School Preparedness unit is responsible for developing research around readiness science. As part of this a large language model was used to abstract themes and code publications that were identified as related to school closure to aid us in supporting our research. If funding were to become available, we would like to develop this static, one-time product into a modifiable tool that could be used for ongoing article abstraction across school and other settings and population. The AI allows us to efficiently categorize thousands of abstracts in a much shorter time-frame with much less human time required into a user-friendly dashboard." }, { "tracker_id": "hhs-2024-1404546d4d", "fiscal_year": "FY2024", "use_case_name": "ChatCDC - CDC Enterprise Generative AI Chatbot (Content Editing)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "CDC has an internal productivity tool available to all CDC staff which can be used in a variety of ways. This use case covers staff who will be using this general purpose to assist with editing text previously written, or content editing. This may refer to using the chatbot to help refine emails or messages to other CDC staff, reviewing internal documents, reviewing scheduling or other bureaucratic information and other such information. CDC sometimes uses GenAI (generative artificial intelligence) to help create content for the public. This may include content for the web, social media, fact sheets, and graphics. In every case, our experts review our communication materials in an effort to ensure accuracy and quality before use. CDC public health research, data, and recommendations are based on the best science currently available and go through a separate agency review process prior to release. Currently, CDC has internal Generative AI guidance determining how this tool can be used, and individual Centers, institutes, or Offices may implement their own policy to ensure safe and responsible AI. This includes internal guidance on using AI for External Communications. This tool enables CDC staff to have the productivity benefits of Generative AI powered chatbots without any of the security risks of data uploads. This tool can be used by all CDC staff to support general business operations and enables a quick review for finding any spelling or grammatical errors, fixing formatting, or other copy-editing type corrections and saves staff time will increasing the quality of communications. CDC sometimes uses GenAI (generative artificial intelligence) to help create content for the public. This may include content for the web, social media, fact sheets, and graphics. In every case, our experts review our communication materials in an effort to ensure accuracy and quality before use. CDC public health research, data, and recommendations are based on the best science currently available and go through a separate agency review process prior to release." }, { "tracker_id": "hhs-2024-2cb2115d12", "fiscal_year": "FY2024", "use_case_name": "ChatCDC - CDC Enterprise Generative AI Chatbot (Data Extraction)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "CDC has an internal productivity tool available to all CDC staff which can be used in a variety of ways. This use case covers staff who will be using this general purpose tool to assist with extracting information from larger pieces of text. Currently, CDC has internal Generative AI guidance determining how this tool can be used, and individual Centers, institutes, or Offices may implement their own policy to ensure safe and responsible AI. From larger documents which may be time consuming to find specific pieces of information, this AI system enables staff to extract key pieces of information to be used in later tasks. This can include action items from meeting transcripts, specific discussion focuses from larger meetings, specific mentions, or other such information. this makes the finding of information from larger document far faster and reduces the manual burden of reading larger documents for one or two specific pieces of information." }, { "tracker_id": "hhs-2024-39996fd5cd", "fiscal_year": "FY2024", "use_case_name": "ChatCDC - CDC Enterprise Generative AI Chatbot (Ideation)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "CDC has an internal productivity tool available to all CDC staff which can be used in a variety of ways. This use case covers staff who will be using this general purpose to assist with the development of ideas or to overcome writers block in different tasks. When doing a task, staff can ask this tool to ask questions to help evolve any ideas being developed. Currently, CDC has internal Generative AI guidance determining how this tool can be used, and individual Centers, institutes, or Offices may implement their own policy to ensure safe and responsible AI. This tool enables CDC staff to have the productivity benefits of Generative AI powered chatbots without any of the security risks of 3rd party AI powered general purpose chatbots. This ensures staff can provide information regarding a current task along with general information with current tasks to help organize thinking, actions to take, and developing ideas for staff to use or reject as a part of their process. This reduces the time spent by staff dealing with situations such as writers block." }, { "tracker_id": "hhs-2024-db240c8ed2", "fiscal_year": "FY2024", "use_case_name": "ChatCDC - CDC Enterprise Generative AI Chatbot (Software Development)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "CDC has an internal productivity tool available to all CDC staff which can be used in a variety of ways. This use case covers staff who will be using this general purpose to assist with the development of software code which can be used for research, data analysis, software development, or other such systems. Currently, CDC has internal Generative AI guidance determining how this tool can be used, and individual Centers, institutes, or Offices may implement their own policy to ensure safe and responsible AI. When the AI chatbot is being used for coding assistance or software development, it affects several business processes related to agency operations and core task structures. Specifically, it can be utilized by various personnel within an organization such as the CDC, including software programmers using AI-specific code, those involved in encoding for software development, data analysts, scientific researchers conducting data analysis, data scientists implementing and testing new models, and software engineers developing or updating applications. The chatbot enhances productivity and efficiency across these roles by providing coding assistance and acting as a learning tool. For instance, data engineers may use the chatbot to generate PySpark code for their data transformation scripts within a larger pipeline. Additionally, individuals new to programming languages like Python can leverage the chatbot as a tutor to help them with tasks such as cleaning up datasets for evaluation purposes. In summary, the AI chatbot impacts business processes by aiding staff members in performing complex analytical tasks more efficiently and supporting their development work through guided coding assistance. This ultimately contributes to enhancing overall operations within the agency by streamlining research activities and internal process workflows." }, { "tracker_id": "hhs-2024-01731253ec", "fiscal_year": "FY2024", "use_case_name": "ChatCDC - CDC Enterprise Generative AI Chatbot (Summarization)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "CDC has an internal productivity tool available to all CDC staff which can be used in a variety of ways. This use case covers staff who will be using this general purpose tool to summarize and synthesize information from larger documents such as meeting transcripts, research articles, or other documents. Currently, CDC has internal Generative AI guidance determining how this tool can be used, and individual Centers, institutes, or Offices may implement their own policy to ensure safe and responsible AI. This tool enables CDC staff to have the productivity benefits of Generative AI powered chatbots without any of the security risks of data uploads to third party tools. Using ChatCDC, Staff can have smaller, more easily digestible summaries to save staff time from reading larger documents if the time to read the full document is not available." }, { "tracker_id": "hhs-2024-00de64e504", "fiscal_year": "FY2024", "use_case_name": "DCIPHER (SEDRIC) AIP for Advanced Foodborne Outbreak Investigation (AI Summarization)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "This AI solution impacts the process of investigating foodborne disease outbreaks. These foodborne outbreaks require cooperative efforts from CDC staff, FDA, USDA, and local agencies and the AI system is used through a centralized data platform System for Enteric Disease Response, Investigation, and Coordination (also known as SEDRIC). For more information on SEDRIC, please go to our website: https://www.cdc.gov/foodborne-outbreaks/php/foodsafety/tools/index.html SEDRIC's AIP use case provides CDC epidemiologists the ability to accelerate their investigations of multi-state foodborne disease outbreaks by more effectively leveraging data available in a rich data source, such as receipts from grocery stores, that otherwise requires extensive time and human effort to parse through. In addition, this workflow would free up epidemiologists' time and, potentially, increase the frequency with which both CDC and STLT partners could utilize shopper card, receipts, and free text responses to support investigations. Using the summarization capability, the extensive process of mapping common names of different food items is done automatically, greatly reducing the human labor time to generate dashboards of information regarding current foodborne investigations to serve as a decision point to aid in outbreak response." }, { "tracker_id": "hhs-2024-8340d6e6a2", "fiscal_year": "FY2024", "use_case_name": "DCIPHER (SEDRIC) AIP for Advanced Foodborne Outbreak Investigation (Receipt Reading)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "This AI solution impacts the process of investigating foodborne disease outbreaks. These foodborne outbreaks require cooperative efforts from CDC staff, FDA, USDA, and local agencies and the AI system is used through a centralized data platform System for Enteric Disease Response, Investigation, and Coordination (also known as SEDRIC). For more information on SEDRIC, please go to our website: https://www.cdc.gov/foodborne-outbreaks/php/foodsafety/tools/index.html SEDRIC's AIP use case provides CDC epidemiologists the ability to accelerate their investigations of multi-state foodborne disease outbreaks by more effectively leveraging data available in a rich data source, such as receipts from grocery stores, that otherwise requires extensive time and human effort to parse through. In addition, this workflow would free up epidemiologists' time and, potentially, increase the frequency with which both CDC and STLT partners could utilize shopper card, receipts, and free text responses to support investigations. Manually entering receipt information, shopper card information, or other such free text field is traditionally error prone and time intensive for a variety of information. This AI system provides a human-in-the-loop opportunity to review and update data entry points while reducing time spent by staff to gain these insights. Having a set structured output as well increased standardization of this information and eases reporting in situations requiring cooperation from multiple organizations." }, { "tracker_id": "hhs-2024-49ae4c6eec", "fiscal_year": "FY2024", "use_case_name": "DFWED Food Vehicle Investigation Module", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Foodborne outbreak investigation Outbreak investigation by providing estimates of output of potential food vehicles can increase speed of tracking foodborne outbreaks in terms of identification of hypothesis generation. This reduces operational burdens in foodborne response efforts." }, { "tracker_id": "hhs-2024-e4eee10ea5", "fiscal_year": "FY2024", "use_case_name": "Genetic distance computation method for comparing complex multi-locus parasite (Cyclospora) genotypes", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance", "laboratory" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Investigating the similarity of infections used during epidemiologic investigations of cyclosporiasis outbreaks. It helps produce a clustering output for massive datasets comprising genotypes that are too complex to be analyzed using traditional genetic distance computation methods and aid in identifying similarities of infections when tracking cyclosporiasis outbreaks and other potential parasites." }, { "tracker_id": "hhs-2024-057bea50a4", "fiscal_year": "FY2024", "use_case_name": "MedCoder - Coding literal text cause of death information reported on death certificates to ICD-10", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "MedCoder is a subsystem of CDC's National Center for Health Statistics (NCHS) National Vital Statistics System for coding causes of death indicated on the death certificates to the International Classification of Diseases 10th Revision (ICD-10) codes. With the implementation of MedCoder, the percentage of deaths that can be automatically and accurately coded has risen from 70-75% using the previous coding system to over 85%. This improvement translates into substantial cost savings, amounting to hundreds of thousands of dollars that would have been spent on manual coding. Additionally, it significantly enhances the timeliness of data related to urgent public health concerns such as COVID and drug overdose deaths, enabling near real-time surveillance." }, { "tracker_id": "hhs-2024-5e9a217b7a", "fiscal_year": "FY2024", "use_case_name": "NCIRD SmartFind ChatBots - Public and Internal", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Internal partner mailbox email management including managing knowledge base. Previously, this included public facing chatbots but the public ChatBots are no longer active. This uses previously cleared FAQs and other reviewed information accessible publicly as the knowledge base. The internal Knowledge-Bot's SharePoint component is helping program staff manage emails from partners more efficiently and effectively including creation and maintenance of a knowledge base that can be utilized across staff managing the mailbox." }, { "tracker_id": "hhs-2024-906ec6964a", "fiscal_year": "FY2024", "use_case_name": "NIOSH Industry and Occupation Computerized Coding System (NIOCCS)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Replaces manual coding of industry and occupation text to standardized codes so that they can be used for research and analysis. Reduces the high cost of manually coding industry and occupation data while promoting increased coding speed, accuracy, and consistency." }, { "tracker_id": "hhs-2024-a97562547f", "fiscal_year": "FY2024", "use_case_name": "Nowcasting Injury Trends", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance", "forecasting" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Predicting current injury rates and real time estimates of injuries and deaths. The internal dashboard that will be created as a result of this effort will enhance the understanding of injury trends and expedite surveillance and research activities. This initiative will have a direct impact on the speed at which emerging trends in injuries resulting in deaths are identified and investigated. Gold standard data take time to process and be made available. Real-time insight of injury trends when gold standard data are not available can be a resource resulting in timelier situational awareness to inform injury surveillance efforts." }, { "tracker_id": "hhs-2024-eb010f90b8", "fiscal_year": "FY2024", "use_case_name": "Risk Assessment Module (RAM) for the National Diabetes Prevention Program (National DPP) Operations Center.", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Tracking program progress. This AI assists in determining if an organization that is currently operating within the National DPP is at risk of improperly starting, at risk of going inactive, or at risk of not achieving the goals necessary to continue successfully in CDC’s Diabetes Prevention Recognition Program. The RAM module will help program managers synthesize large amounts of organization-level data in making decisions on how best to assist organizations with their programs, ultimately leading to increased program participation and improved health outcomes in program participants." }, { "tracker_id": "hhs-2024-71f7196413", "fiscal_year": "FY2024", "use_case_name": "Semi-Automated Nonresponse Detection for Surveys (SANDS)", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "SANDS is a fine-tuned large language model (LLM) designed to assist with human-in-the-loop procedures specifically for open-ended survey responses. Open-ended survey responses vary in quality and are challenging to use effectively because they are labor-intensive to review and therefore cost-prohibitive to use at scale. SANDS is intended to reduce the burden by filtering out clear and obvious non-response such as \"This is so dumb, why I am I wasting my time on such a stupid question\" or \"Because It is true\" which provide no value to researchers. This LLM is available on HuggingFace and is not routinely updated, but it can be used like any open-source LLM through secure instances of Python. Manual curation of open-ended survey responses is time-consuming, often requiring extensive hours to identify themes and review outputs. SANDS significantly reduces this manual burden by providing scores for responses, enabling researchers to quickly compile an initial high-quality dataset for qualitative research. SANDS also flags responses needing further examination, streamlining the review process." }, { "tracker_id": "hhs-2024-92de5415a8", "fiscal_year": "FY2024", "use_case_name": "Sequential Coverage Algorithm (SCA) and partial Expectation-Maximization (EM) estimation in Record Linkage", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "CDC's National Center for Health Statistics (NCHS) Data Linkage Program has developed a record linkage program designed to maximize the scientific value of the Center’s population-based surveys. Linked data files enable researchers to examine the factors that influence disability, chronic disease, health care utilization, morbidity, and mortality. The linkage algorithms initially used by the program did not implement machine learning in either deterministic or probabilistic linkages and required several manual steps for these processes. Using machine learning in the CDC's National Center for Health Statistics (NCHS) Data Linkage Program offers several benefits. Machine learning algorithms help improve the accuracy and efficiency of data linkage by developing joining methods or blocking groups. Working with very large datasets can be time-consuming and resource-intensive. By implementing supervised and unsupervised machine learning techniques, the Data Linkage Program can automate the process of developing and selecting blocking groups, reducing manual effort and increasing efficiency. This additionally improves the scalability of data linkages. Machine learning algorithms can continuously learn and adapt based on new data inputs and feedback. This allows the Data Linkage Program to refine their linkage algorithms over time." }, { "tracker_id": "hhs-2024-004959930e", "fiscal_year": "FY2024", "use_case_name": "TowerScout: Automated cooling tower detection from aerial imagery for Legionnaires' Disease outbreak investigation", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance", "environmental-occupational" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "During Legionella outbreaks, the National Center for Immunization and Respiratory Diseases (NCIRD)/Division of Bacterial Diseases (DBD) needs to identify cooling towers that could potentially be spreading Legionella bacteria. Using AI object detection, TowerScout detects cooling towers approximately 600 times faster than manual searches, allowing researchers to more efficiently respond during outbreaks." }, { "tracker_id": "hhs-2024-4c46520e23", "fiscal_year": "FY2024", "use_case_name": "Creation of synthetic Survey-like Insurance Names for use coding NHIS private insurance responses", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This solution will impact the process of manual coding and reviewing text errors in the National Health Interview Survey Previous efforts to augment the manual coding have proven ineffective. Given the complex nature of insurance programs, this results in over 200 FTE hours a year being spent on just the initial coding of open text insurance fields. Additional hours are spent reviewing the manual coding for errors. By creating survey-like insurance responses, this could theoretically save over 100 hours of staff labor a year, greatly increasing efficiency and timeliness." }, { "tracker_id": "hhs-2024-fcc9e9556d", "fiscal_year": "FY2024", "use_case_name": "Development of in-silico genomic and patient datasets using generative ML algorithms", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This solution will impact the process of finding reference genomes Reduce the development time of custom tools, finding reference genomes, and additional administrative burdens. The datasets developed would be beneficial in testing out bioinformatic pipelines." }, { "tracker_id": "hhs-2024-6d7b53c9c0", "fiscal_year": "FY2024", "use_case_name": "Mastering Metadata: AI-Powered Governance for Data Insights", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The implementation of this solution will have a significant influence on the maintenance of data quality and data assets on the data.cdc.gov platform. Accurate, reliable, and timely data available in data.cdc.gov is critical in making meaningful public health decisions. This publicly available data repository is used worldwide by researchers, policy makers, and general public to access, manage, analyze, and visualize public health data. Through this project, we will be able to scale up the magnitude and quality of datasets from NCIRD that’s made available via data.cdc.gov and thus maximizing public health impact for a diverse audience at multiple levels." }, { "tracker_id": "hhs-2024-46dc22294e", "fiscal_year": "FY2024", "use_case_name": "PII detection using Private AI", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Numerous data sources contain personally identifiable information (PII) and protected health information (PHI), which cannot be publicly published or shared among federal partners due to confidentiality concerns. This includes sensitive records like death certificates, survey responses, and electronic health records. To enable broader utilization of this data and enhance insights, a PII detection system was explored in this project. Specifically, the availability of an off-the-shelf tool called Private AI was investigated for identifying PII. However, it is important to note that the license for this tool expired after one year and there are no current plans to renew the contract. At present, the presence of PII within data and the labor intensive process to ensure PII redaction and removal has prevented broader sharing of data available within CDC. However, implementing an automated system which performs as well as humans and more quickly could significantly reduce the time required for human review, enabling faster dissemination of crucial public health information and data sources." }, { "tracker_id": "hhs-2024-7342c55de6", "fiscal_year": "FY2024", "use_case_name": "Reddit Post Analysis for Sexual Health Using Large Language Models", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The AI will impact the data extraction and analysis processes for understanding how users seek sexual health advice online using Reddit. In additional to traditional public health impacts this can include reducing administrative burdens and other ancillary benefits. This is an important criteria when selecting the initial round of projects. Understand how and why people seek sexual health advice online. This may lead to finding gaps in online health resources, identifying underserved populations who resort to using online resources, and discovering changes in sexual health behavior over time." }, { "tracker_id": "hhs-2024-1f9e723d8d", "fiscal_year": "FY2024", "use_case_name": "Retrieval Augmented Generation (RAG) with Q-Bank", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This solution will impact the process of searching CCQDER’s Q-Bank research publications on question evaluation for surveys. Q-Bank reports provide context needed to better understand and interpret data from survey estimates, as well as information about question performance and fit-for-use in various data collection contexts. This project aims to improve access and visibility of CCQDER’s Q-Bank research publications on question evaluation for surveys. Additionally, indexing Q-Bank as part of this project should help to relieve some of the administrative burden of tagging and organizing reports." }, { "tracker_id": "hhs-2024-368951b1a2", "fiscal_year": "FY2024", "use_case_name": "Using generative AI to gain insight of older adult falls", "previous_name_reported": "", "bureau_as_reported": "CDC", "component": "CDC", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "This solution will impact the process of extracting information from medical narratives Traditional methods of data and information extraction from medical narratives involve manual scrutiny and the use of rule-based models. These methods are time-consuming and can overlook crucial details. By utilizing generative AI, a more comprehensive understanding of the circumstances surrounding injuries from falls can be achieved. Understanding the specific causes, circumstances, and outcomes of falls can lead to more targeted public health interventions." }, { "tracker_id": "hhs-2024-c941efd7e4", "fiscal_year": "FY2024", "use_case_name": "AI-assisted comment triaging tool", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Annual rulemaking-- the Physician Fee Schedule receives thousands of public comments and require assistance with reviewing and assigning the comments to the particular topics they address. Assists with workload for the PFS team" }, { "tracker_id": "hhs-2024-ace2c06998", "fiscal_year": "FY2024", "use_case_name": "Bankruptcy BOT", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "program-integrity" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Bankruptcy/Overpayment Division of IFM is responsible for carrying out the Agency's oversight responsibilities for our contractors' debt management functions, and for protecting Medicare's interest in debt recoupment activities complicated by bankruptcy. A timely identification of filed provider bankruptcies allows Medicare to secure recovery rights before timeframes to file a proof of claim expire." }, { "tracker_id": "hhs-2024-7a07048ca2", "fiscal_year": "FY2024", "use_case_name": "Central Data Abstraction Tool-Modernized (Modernized-CDAT)- Intake Process Automation (PA) Tool", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Law & Justice", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Intake PA uses advanced capabilities (NLP, OCR, AI, ML) to automate, modernize, and reduce manual efforts related to medical record review functions within MA RADV audits Eliminates manual review of Medical Record (MR) submissions, expedites the Intake process and realizes cost savings in both time and resources." }, { "tracker_id": "hhs-2024-d42a677172", "fiscal_year": "FY2024", "use_case_name": "CMS Enterprise Portal Services (CMS Enterprise Portal-Chatbot)", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "CMS Enterprise Portal AI for Process Efficiency Improvement| Knowledge Management - Empowers users to find information or troubleshoot issues more efficiently - Reduces load on system help desks - Provides better insight into customer inquiries and issues informing process and design improvements" }, { "tracker_id": "hhs-2024-0af6224b02", "fiscal_year": "FY2024", "use_case_name": "Enhanced Direct Enrollment Outlier Detection", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "program-integrity" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Enhanced Direct Enrollment (EDE) allows consumers to apply for and enroll in an exchange plan directly through an approved partner’s UI, without being redirected through the Healthcare.gov application. These partner systems directly interface with the APIs developed by the FFE. As EDE Partners gain more control over their application process, the FFE must ensure program integrity. Ensure FFE EDE program integrity" }, { "tracker_id": "hhs-2024-9d02839bed", "fiscal_year": "FY2024", "use_case_name": "Exchange Complaints Review Categorization ML/AI", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Law & Justice", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Exchange Complaints Review Contractor (ECRC) receives hundreds of consumer complaints from the Federally-facilitated Exchange (FFE) call center daily, who then must manually read each complaint and categorize each complaint into one or more categories according to the Complaint Review Standard Operating Procedure (SOP). The categorization assists CPI in determining the nature of the complaint, the action necessary, and the component within CMS who the complaint should be routed to. In general, each complaint can take up to 10 minutes to review and categorize, including a quality review component. The output (list of all complaints categorized) allows CMS to expediate routing the complaint to the appropriate CMS caseworker or Issuer to take the appropriate action necessary to make the consumer whole and ensure they can receive the necessary medical treatment. The output when done by AI is done in a matter of minutes, versus weeks when completed by human reviewers." }, { "tracker_id": "hhs-2024-63bdaaebb7", "fiscal_year": "FY2024", "use_case_name": "Feedback Analysis Solution (FAS)", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Feedback Analysis Solution is a system that uses CMS or other publicly available data (such as Regulations.Gov) to review public comments and/or analyze other information from internal and external stakeholders. The FAS uses Natural Language Processing (NLP) tools to aggregate, sort and identify duplicates to create efficiencies in the comment review process. FAS also uses machine learning (ML) tools to identify topics, themes and sentiment outputs for the targeted dataset. FAS help categorize stakeholder comments or feedback (collected in multiple venues), thereby enabling analysts to use the system to quickly identify comments that may impact program/policy decisions." }, { "tracker_id": "hhs-2024-0385f3a371", "fiscal_year": "FY2024", "use_case_name": "IT System Utilization Optimization", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The FFE application usage patterns vary and are dependent on differing environment usage periods. CMS resources are currently manually scaled, not allowing immediate actions in correlation with usage changes. Automation of application scaling" }, { "tracker_id": "hhs-2024-cc7536fb30", "fiscal_year": "FY2024", "use_case_name": "Knowledge Management Platform", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The business process it affects involves accessing and using structured and unstructured data sources to enable data-driven decision-making. It helps by creating efficiencies, reducing burden, and generating an increased return on investment in the achievement of CMS's mission and business objectives." }, { "tracker_id": "hhs-2024-071d5a8adb", "fiscal_year": "FY2024", "use_case_name": "Risk Adjustment Outlier Analysis", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The RA program spreads the financial risk borne by Issuers due to offering a variety of plans meeting the need of the diverse population. RA payments are distributed based on population risk levels. The FFE uses a distributed data solution to calculate plan average actuarial risk and associated payments and must avoid potential impacts to annual calculations. Maintain RA Program risk integrity" }, { "tracker_id": "hhs-2024-644642e97e", "fiscal_year": "FY2024", "use_case_name": "Agent/Broker Fraud Analysis", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "program-integrity" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Agent/Brokers (A/Bs) support the consumer enrollment and eligibility process. Because of this, they have learned the intricate details of the FFE for accessing applications, submitting eligibility determinations, and adding enrollments to their line of business, opening up the possibility of fraud. Improves the oversight and integrity of Marketplace Applications and Enrollments assisted by Agents and Brokers. Helps CCIIO identify suspicious behaviors to refer to CPI." }, { "tracker_id": "hhs-2024-6c63db5837", "fiscal_year": "FY2024", "use_case_name": "CCSQ ServiceNow AI Search", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "CCSQ ServiceNow AI Search is a product inside ServiceNow (SaaS). It replaces traditional Zing search tool (exact search), and enables users with more flexible searches to get relevant and actionable answers quickly. * Improve search relevance * Promote self-service by empowering users to find information independently, and potential reduce number of cases" }, { "tracker_id": "hhs-2024-d1dcb7de14", "fiscal_year": "FY2024", "use_case_name": "MSP Assignment Bot", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This impacts automatically retrieving records from a contractor's system, and importing them into an internal OPOLE Access database. Once populated in the database the record is assigned to staff to review and adjudicate. Pulls more cases out of BCRC and assigns much faster than an analyst. By allowing BOT to pull and assign cases instead of an analyst, this approach provides maximum time for staff to process cases which decreases the response time." }, { "tracker_id": "hhs-2024-be3d1b656b", "fiscal_year": "FY2024", "use_case_name": "Relationships, Events, Contacts, and Outreach Network (RECON)", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "RECON AI for Recommender System| Sentiment Analysis It has the potential to analyze data faster." }, { "tracker_id": "hhs-2024-15d8dc701e", "fiscal_year": "FY2024", "use_case_name": "Complaint Analysis POC", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The business process this AI could affect involves the analysis of complaint data used by the OPOLE team. It could help the OPOLE team to understand the main issues and potential root causes of the complaints, enabling them to address these issues more efficiently and effectively." }, { "tracker_id": "hhs-2024-58af1b534b", "fiscal_year": "FY2024", "use_case_name": "Data Lake/Load-Extract-Transform-Load (L-ETL)", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "CMS is using Security Data Lake to modernize the Load-Extract-Transform-Load (L-ETL) pipelines and data tooling. There is no actual ML/AI work being done here today, rather, we are beginning work on the scaffolding that will open up these opportunities in 1-2 years time. Building on top of a modern data platform will provide opportunities to experiment with machine learning model development against this data--solving any number of problems that require decisions to be made about inferences over time series data." }, { "tracker_id": "hhs-2024-239f204952", "fiscal_year": "FY2024", "use_case_name": "FOIA Document Review/Redaction Automation", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "OIT & OSORA partnership: Internal business process of reviewing and redacting CMS business documents subject to FOIA requests. Reduces burden on FOIA staff to manually redacting business documents subject to FOIA requests." }, { "tracker_id": "hhs-2024-6aedb1e955", "fiscal_year": "FY2024", "use_case_name": "Help Desk Responses", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Division of Issuer Management and Operations has talked with their contractor, LMI, about using an AI tool for the Help Desk contract. This would reduce the amount of time staff contractors need to generate answers for SME review and approve responses to QHP issuers and other external entities that ask questions to the help desk." }, { "tracker_id": "hhs-2024-6fc1a43756", "fiscal_year": "FY2024", "use_case_name": "508 Accessibility Automation for Documents", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "OIT: Internal business process at individual employee level to automate review, identification, and correction of potential 508 accessibility issues in documents. Reduces burden associated with making documents 508 compliant (for individual CMS employees)." }, { "tracker_id": "hhs-2024-8cd4c5e157", "fiscal_year": "FY2024", "use_case_name": "Brand vs Generic Market Share", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Drug Data Processing System (DDPS) needs to predict changes in Medicare Part D costs Understanding beneficiary changes from Brand to Generic drugs allows for more accurate financial estimates." }, { "tracker_id": "hhs-2024-231b962f8b", "fiscal_year": "FY2024", "use_case_name": "Chat Client - Resource Library", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Education & Workforce", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Continue to assess and refine content present in both the informational public side of QPP, Resource Library, Webinar Library, and Top Navigation as the site continues to grow. The Chat Client would benefit the program by improving user experience through easier access to information, increased program understanding, and ability to communicate in multiple languages." }, { "tracker_id": "hhs-2024-74befebe7c", "fiscal_year": "FY2024", "use_case_name": "Chatbot within Hub", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Currently the following help support activities are handle by a person: Personalized FAQ’s; Clarify questions with schema and onboarding etc.; Handle - Where is “My file” or “My Request” inquiries Provide a - Talk to Agent feature Scheduling a testing window Provide data summarization and reporting for internal stakeholders Report operational health of the system Include training materials, Q/A about the system and such. Help support team in day-to-day communication with external partners" }, { "tracker_id": "hhs-2024-bb58e75035", "fiscal_year": "FY2024", "use_case_name": "Drug cost anomaly detection", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "program-integrity" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "CMS Division of Payment Reconciliation is responsible for monitoring Prescription Drug Event (PDE) data for accuracy Allows for identification of outliers in PDE which can be corrected through Part D plan outreach in order to prevent overpayments." }, { "tracker_id": "hhs-2024-8b19527901", "fiscal_year": "FY2024", "use_case_name": "Drug Cost Increase Predictions", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Drug Data Processing System (DDPS) needs to predict changes in Medicare Part D costs Machine Learning model uses multiple methods to predict changes to Part D drug costs based on market changes." }, { "tracker_id": "hhs-2024-7aec7198b7", "fiscal_year": "FY2024", "use_case_name": "Medicare Part C/D Marketing Material Review", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Drug and Health Plan Operations (DHPO) staff reviews both advertising mediums and channels materials that includes over 22 different types of media marketing materials ranging from written, digital, and more. The marketing materials are submitted to CMS from either the Medicare Health Plans and/or their third party marketing organizations. DHPO staff reviews these marketing materials to ensure they meet Medicare Marketing Guidelines. The current process is time consuming and resource intensive. This reduces the efficiency and quality of staff reviews. AI can improve the efficiency and quality of the marketing materials reviewed; with human validation of results. Incorporating AI into current review process can reduce the time it takes to review marketing materials, improve efficiency and allow existing FTE resources to be utilized more efficiently, and potentially reduce the volume of marketing misrepresentation complaints. Ideally this will improve efficiency and quality, help beneficiaries access to care, and also ensure marketing materials meets Medicare Marketing Guidelines." }, { "tracker_id": "hhs-2024-052ecb0066", "fiscal_year": "FY2024", "use_case_name": "Medicare Part C/D Plan Oversight of AI Used for Prior Authorization and Utilization Management", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Law & Justice", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Medicare beneficiary access to benefits, services, and prescriptions associated with managed care plans and prescription drug plans. AI tools can increase the capacity to analyze vast data sets based on combined sources. The results can then be further analyzed and refined by SMEs to issue compliance actions, identify plans for program auditing, and help protect the integrity of the Medicare program." }, { "tracker_id": "hhs-2024-fccfa33eff", "fiscal_year": "FY2024", "use_case_name": "Negotiated Drug Predictions", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Inflation Reduction Act authorizes Medicare to directly negotiate drug prices for certain high expenditure, single source Medicare Part B or Part D drugs. For the first year of the Negotiation Program, the Secretary will select 10 Part high expenditure, single source drugs for negotiation. Will assist with drug price negotiation for selected drugs" }, { "tracker_id": "hhs-2024-be931fbb4f", "fiscal_year": "FY2024", "use_case_name": "QPP Admin Bot", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Provides custom reporting and analysis to meet the ongoing and emerging needs to address analytics and reporting needs and supports key business processes in the QPP cycle - such as delivering feedback to providers, calculating final scores, and delivering payment adjustments to the Medicare contractors. The Admin Bot would benefit the program by increasing availability of data, thus empowering users and informing decisions, through a simple-to-use communication platform." }, { "tracker_id": "hhs-2024-700e236e64", "fiscal_year": "FY2024", "use_case_name": "Operational Efficiency Analytics", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "OHI/MAG is responsible for providing Marketplace consumers with an accurate, efficient and timely appeals adjudication process. Examples where we can use predictive analytics: * Identifying trends in operations for managing and completing appeals processing * Predicting the number of appeals during open enrollment * Looking for regional or demographic trends in appeal complexity, completion time, and success" }, { "tracker_id": "hhs-2024-baf5181c50", "fiscal_year": "FY2024", "use_case_name": "ICPG Governance Tool", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The business process it will affect involves the processing and analysis of acquisition plans (APs). It will help by streamlining the manual steps involved in validating and classifying acquisition plans, reducing the time and effort required." }, { "tracker_id": "hhs-2024-94feddbc09", "fiscal_year": "FY2024", "use_case_name": "Innovation Center Investment Proposal (ICIP) Model Analysis POC", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "It will affect the process of document analysis, specifically the analysis of ICIP documents. It will help in ICIP document analysis, with the goal of extracting of insights and recommendations from these documents." }, { "tracker_id": "hhs-2024-486575d51a", "fiscal_year": "FY2024", "use_case_name": "OAGM Rate Card", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The business process it will affect involves the analysis of historical price history/trends for labor categories in business proposals for the Office of Acquisition and Grants Management (OAGM). It will help OAGM make better decisions on future contracts by providing them with a comprehensive and standardized view of historical price data for labor categories." }, { "tracker_id": "hhs-2024-82853b078d", "fiscal_year": "FY2024", "use_case_name": "CCSQ Now Assist for CSM", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "CCSQ Now Assist for CSM (Customer Service Management) is a product inside ServiceNow (SaaS). It integrates Gen AI with CSM, and uses Now LLM (ServiceNow native Large Language Model) to generate contents based on machine learning (ML). Now Assist for CSM helps agents to improve productivity and efficiency and deliver better services. Improve agents' responsiveness and productivity * Quickly get familiar with a case/chat by getting case/chat summarization * Quickly resolve the case by using auto-generated resolution notes." }, { "tracker_id": "hhs-2024-b72a58ed65", "fiscal_year": "FY2024", "use_case_name": "AI Workspace", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The business process this affects involves the ability to easily run AI experiments in a safe and secure environment. It helps by reducing barriers to AI experimentation, supporting strategic AI infrastructure enablement, and providing hands-on experience for teams to learn and understand the potential risks and benefits of AI." }, { "tracker_id": "hhs-2024-56d14d7abb", "fiscal_year": "FY2024", "use_case_name": "Citation Analysis and Survey Assistant (CASA - Nursing Home Survey CMS 2567)", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Law & Justice", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "2567 (CASA) enhances the efficiency and effectiveness of monitoring and reviewing nursing home surveys across the US. It enhances how the Quality, Safety, and Oversight Group (QSOG) and Survey Operations Group (SOG) assess how State Survey Agencies (SSAs) are citing nursing home deficiencies, reported on CMS Form 2567, by employing advanced natural language processing and machine learning technologies. The system would support the Nursing Home Survey CMS Form 2567 survey work allowing QSOG and SOG staff to follow up on cases of under and over citations at the facilities that are flagged by the system and sent to the State Agencies" }, { "tracker_id": "hhs-2024-805bf865ef", "fiscal_year": "FY2024", "use_case_name": "Hazards & Threats Awareness Data Processing Automation", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "emergency" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "EPRO is responsible for developing agency-wide situation reports from various open web and email materials. More effective prioritization and processing of materials for situational awareness, faster delivery of usable work product to decision-makers agency-wide. The manual process takes 2-3 hours per day, and the AI system will reduce the work to a human quality assurance/inspection step estimated to take approximately 30 minutes." }, { "tracker_id": "hhs-2024-5949eb4edc", "fiscal_year": "FY2024", "use_case_name": "Independent Dispute Resolution (IDR) Eligibility Rules Engine", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The current Independent Dispute Resolution (IDR) Technical Assistance (TA) process is very manual and time intensive which limits throughput. The rules engine should significantly expedite processing and expand capacity. Automating more of the process may also increase consistency across recommendations and result in a more predictable timeframe for the workflow by better positioning disputes for analyst review. The AI tool will help automate the eligibility review process, reducing time-intensive manual steps and increasing consistency of results." }, { "tracker_id": "hhs-2024-eabcbbe1d5", "fiscal_year": "FY2024", "use_case_name": "Medicare Fee for Service Requirements Modernization (MFRM)", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "EIG is developing a business process to parse, curate, and simplify Medicare coverage and coding requirements by medical condition using AI LLMs. After rigorous testing for accuracy, AI output will be uploaded to the MFRM application to be used to enhance CMS and healthcare operational efficiencies, including organization of guidance documents, and identification of source complexities to be triaged with owners." }, { "tracker_id": "hhs-2024-81afaad6fd", "fiscal_year": "FY2024", "use_case_name": "Review Regulatory Comments", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Division of Issuer Management and Operations is considering having our contractor, AIR, use an AI tool for comment analysis in the Letter to Issuers (LTI) and Notice of Benefits and Payment Parameters (NBPP). This would streamline review of stakeholder comments. This tool would reduce the amount of time and resources CCIIO and contracting staff would have to review the LTI and NBPP by 25%." }, { "tracker_id": "hhs-2024-559366425d", "fiscal_year": "FY2024", "use_case_name": "Improved Data Quality Checks", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Improve consumer experience by providing an asynchronous solution to detect and provide near real time feedback via outreach to the consumer shortening the overall return cycle time without requiring UI changes. Improvement of user experience" }, { "tracker_id": "hhs-2024-4e728209cf", "fiscal_year": "FY2024", "use_case_name": "Plan Certification Recommendation Model", "previous_name_reported": "", "bureau_as_reported": "CMS", "component": "CMS", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Identify trends/patterns within plan certification justification templates, containing free-form text data through use of NLP and classification techniques. Improve efficiency of the plan certification review process" }, { "tracker_id": "hhs-2024-707baf9012", "fiscal_year": "FY2024", "use_case_name": "AI-Assisted Data Entry", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The intended purpose is to leverage AI technology to intake paper forms and assist in the data entry process, which will increase efficiency and reducing human error." }, { "tracker_id": "hhs-2024-804a13ae33", "fiscal_year": "FY2024", "use_case_name": "Transcription in Zoom and Teams", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Meetings scheduled via Zoom or Teams can be transcribed live or from recorded meetings, which improves communication." }, { "tracker_id": "hhs-2024-01b34dabf9", "fiscal_year": "FY2024", "use_case_name": "Email Prioritization in Outlook", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Outlook in Office365 suite can categorize incoming emails based on users' definitions, which can help users manage their inbox more efficiently. The output is categorized emails." }, { "tracker_id": "hhs-2024-405c19ce7b", "fiscal_year": "FY2024", "use_case_name": "AI-assisted Pattern Detection", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "AI technologies have been integrated into many existing log systems to identify unusual patterns, which can help detect system log issues and raise alerts for human intervention." }, { "tracker_id": "hhs-2024-5e4b8341f8", "fiscal_year": "FY2024", "use_case_name": "Report Summarization by commercial GenAI platform", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Some GenAI platforms, such as ChatGPT and Google Gemini, can summarize documents with non-sensitive information." }, { "tracker_id": "hhs-2024-3b23789612", "fiscal_year": "FY2024", "use_case_name": "Information Search from Google", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Google's search engine provides AI-assisted techniques to process search requests efficiently." }, { "tracker_id": "hhs-2024-915f2ca9af", "fiscal_year": "FY2024", "use_case_name": "Document Digitization by AdobePro", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Adobe Pro can convert scanned documents into digital data." }, { "tracker_id": "hhs-2024-74741d0781", "fiscal_year": "FY2024", "use_case_name": "Autocorrect in MS Word", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "MS Word's autocorrect feature uses AI to suggest corrections, which improves the quality of written communications." }, { "tracker_id": "hhs-2024-9e32998137", "fiscal_year": "FY2024", "use_case_name": "AI-Assisted Real-time Collaboration", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The AI-assisted real-time collaboration tool can process the inputs from team members and provide real-times stats using NLP, which offers real-time feedback for collaboration." }, { "tracker_id": "hhs-2024-4b7b7fda7e", "fiscal_year": "FY2024", "use_case_name": "Presentation Design by MS PowerPoint", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "MS PowerPoint can provide more appealing design suggestions based on the content." }, { "tracker_id": "hhs-2024-5ecb6d4b3a", "fiscal_year": "FY2024", "use_case_name": "Data modeling and visualization by MS Excel", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "MS Excel contains many data analysis toolkits to provide AI-powered data analysis and visualization, which can improve understanding and presentation of data." }, { "tracker_id": "hhs-2024-0b66a5a0ea", "fiscal_year": "FY2024", "use_case_name": "News from commercial publisher such as Google News", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Major news publishers, such as Google News, send selective news based on users' preferences and recent visits." }, { "tracker_id": "hhs-2024-be35009300", "fiscal_year": "FY2024", "use_case_name": "Travel Route Optimization by Google Maps and Apple Maps", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Major commercial map service providers, such as Google Maps or Apple Maps, can automate and optimize travel routes based on algorithms and live traffic data, which can save time and provide optimal routes." }, { "tracker_id": "hhs-2024-9b08901f90", "fiscal_year": "FY2024", "use_case_name": "AI-Powered Travel Booking", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Major travel booking websites, such as hotels.com, can provide booking options based on customers' preferences. Algorithms running in such services can optimize travel accommodations, which can save time and provide better deals." }, { "tracker_id": "hhs-2024-999a9e3415", "fiscal_year": "FY2024", "use_case_name": "Facial Recognition Unlock", "previous_name_reported": "", "bureau_as_reported": "HHS", "component": "HHS (unspecified)", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Face ID feature in iPhones takes advantage of AI technology to securely unlock the phone." }, { "tracker_id": "hhs-2024-fd061e3c6e", "fiscal_year": "FY2024", "use_case_name": "AI to Improve Public Access to the Administrative Appeals Process", "previous_name_reported": "", "bureau_as_reported": "DAB", "component": "Office of the Secretary", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The DAB adjudicates disputes under more than 60 statutory authorities. The workload is divided between the DAB's four adjudicatory divisions (e.g., the Board, the Civil Remedies Division, the Medicare Operations Division and the Alternative Disputes Resolution Division. Helping appellants decide which entity within the DAB is responsible for adjudicating their appeal will increase efficiency in adjudication and customer experience. The DAB's appellant community (e.g., e-file users) will be provided immediate assistance and accurate information; deaf and hard-of-hearing populations and individuals with limited English proficiency are helped without the use of intermediaries, such as interpreters, translators, and relay services. After determining the appropriate division within the DAB responsible for adjudicating the appeal, the Chatbot can provide a link to the e-file website directly connecting the user to the correct e-file system. Other benefits include: Improve public access to the appeals process by providing instant, accurate and complete filing information; Increase the DAB's productivity by refocusing the workforce to case resolution instead of responding the filing inquires and processing and appropriately routing misfiled appeals; and Improve resource allocation based on each Division's actual workload." }, { "tracker_id": "hhs-2024-67fadddeb5", "fiscal_year": "FY2024", "use_case_name": "AI Use Policy Tool", "previous_name_reported": "", "bureau_as_reported": "DAB", "component": "Office of the Secretary", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The DAB is responsible for issuing fair, impartial, legally correct and defensible determinations which serve as the final decision of the DHHS Secretary. The AI Use Policy is designed to promote public confidence in the integrity of DAB decisions. More effective quality review tool and faster identification of data trends that require additional analysis." }, { "tracker_id": "hhs-2024-0351f2473d", "fiscal_year": "FY2024", "use_case_name": "Resources to Assist the Advisory Board In Identifying AI Tools for Use In An Adjudication Environment", "previous_name_reported": "", "bureau_as_reported": "DAB", "component": "Office of the Secretary", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The adjudication process from docket analysis, case processing, issuing a decision and quality review (after adjudication) relies on the analysis of large quantities of data. AI or machine learning tools will help the Advisory Board determine which technology will best serve the Agency's needs in an adjudication environment." }, { "tracker_id": "hhs-2024-28150234cc", "fiscal_year": "FY2024", "use_case_name": "356H ML Facility Supply Chain Role Classification", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Applicants submit unstructured text to indicate a facility’s supply chain role, which range from manufacturing, testing, storing, packaging, etc. This use case extracts the supply chain role based on the text and presents it to data stewards who perform data operations Improve the efficiency of evaluating 356H forms to assess a facility's supply chain role, thereby decreasing the time required for the processing of submissions." }, { "tracker_id": "hhs-2024-823ef0f427", "fiscal_year": "FY2024", "use_case_name": "AI Tool for Risk-based FAR Review & Decision Support", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Assist Field Alert Report (FAR) reviewers by providing risk-based intelligence and insights for review and decision support with a Human-in-the-Loop. Assist with streamlining an efficient FAR review process by summarizing risk profiles for human exploration that is evaluated with additional data to assist human decisioning." }, { "tracker_id": "hhs-2024-9da46c8610", "fiscal_year": "FY2024", "use_case_name": "Analytics-Driven Supplement Evaluation (ASE)", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This AI use case supports the triage and staff assignment process for the review of post-market Change Being Effected (CBE) supplement submissions. The output of the AI component contributes to the triage and staff assignment process, and summarizes submitted information to support the decision of the reviewers of the CBE 30/0 supplemental submissions." }, { "tracker_id": "hhs-2024-63ae945bbc", "fiscal_year": "FY2024", "use_case_name": "Augmenting death and cause of death ascertainment in observational data sources", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance", "product-safety" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Augment assessment of mortality in electronic health records to support investigations to develop probabilistic estimates for causes of death in the Sentinel system. The extraction of mortality information improves studies in Sentinel using mortality as an endpoint by enhancing on the quality of mortality data." }, { "tracker_id": "hhs-2024-06b20ab1eb", "fiscal_year": "FY2024", "use_case_name": "CLAT (Computerized Labeling Assessment Tool)", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "CLAT uses computer programming, image processing, and AI to assist the human review of drug labeling, including container labels, carton labeling, prescribing information, and Instructions for Use. CLAT assists the human visual comparison of drug labeling against a library of FDA regulations, guidances, standards, and best practices. CLAT identifies potential labeling deviations for human evaluation. CLAT is used to improve efficiencies in the manual labeling review process to help the reviewer mitigate medication errors and standardize labeling across therapeutic drug classes and product types. CLAT also assists the reviewer to catch deviations between label versions so no unintended errors have been introduced." }, { "tracker_id": "hhs-2024-82ccf926d3", "fiscal_year": "FY2024", "use_case_name": "Empirical evaluation of EHR-based signal detection approaches", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This supports Electronic Health Record (EHR) signal detection by extracting information using Natural Language Processing (NLP) and expanding the tree based scan statistic (TBSS) methods for EHR-based signal detection. Extracts data to facilitate the identification of signals for outcomes, improving the manual identification of signals for outcomes derived from electronic health records." }, { "tracker_id": "hhs-2024-8ed298b500", "fiscal_year": "FY2024", "use_case_name": "FAR-based Facility Signal Detection Tool", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Identifies problem clusters for the flagged signals in an objective manner for triage/review. Assist with objectively identifying problem clusters for the flagged signals, which then go through a dedicated triage & review process for further actions, and if needed, CMS (inspection) work activities can be opened." }, { "tracker_id": "hhs-2024-40f4e4d9fe", "fiscal_year": "FY2024", "use_case_name": "MedWatch Dashboard", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Assist with consistent monitoring and identification of product risks from MedWatch reporting patterns and report content to support review staff. Supports efficient, objective, risk-based product quality assessments from MedWatch reports for verification by review staff" }, { "tracker_id": "hhs-2024-2db4970aad", "fiscal_year": "FY2024", "use_case_name": "Quality Surveillance Dashboard (QSD)", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Extracts unstructured text from documents and pools that with other available data to form a dashboard that enables consistent assessment of CDER regulated manufacturing facilities Assists in the efficient, objective, risk-based assessments of CDER regulated manufacturing facilities." }, { "tracker_id": "hhs-2024-9374b8e52b", "fiscal_year": "FY2024", "use_case_name": "Scalable automated NLP-assisted chart abstraction and feature extraction tool", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Examine the usability of electronic medical records in conducting pharmacoepidemiology studies through a case study of montelukast use among patients with asthma and neuropsychiatric events The project will involve an “end-to-end” implementation of a full pharmacoepidemiologic study using one or more product-outcome pairs to demonstrate how NLP and other advanced analytic methods or tools can be used to accurately identify study outcomes and covariates from unstructured Electronic Health Record (EHR) data." }, { "tracker_id": "hhs-2024-e5bd86a49a", "fiscal_year": "FY2024", "use_case_name": "Annual Report CMC", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Objective of this use case was to extract CMC changes reported within unstructured annual report industry submission documents to build a complete repository for downstream analysis The extracted data are presented in a dashboard (along with other data) for downstream human analysis." }, { "tracker_id": "hhs-2024-fbb9c0f43f", "fiscal_year": "FY2024", "use_case_name": "Application-DMF Reference", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Extracts references to DMFs from marketing application (ANDA/NDA/BLA) submissions. These submission documents may be structured (356H form) or unstructured (eCTD module 1-4). This pipeline parses content from these documents, extracts DMF references (e.g., ANDA123456 references DMF123456), and exposes the data in a structured format for analysis. The extracted data are presented in a dashboard, along with additional data, for downstream human analysis. The purpose of this use case is to build a comprehensive inventory of all application to DMF references to support OPQ in reviewing applications since many application to DMF references were previously missing from structured sources." }, { "tracker_id": "hhs-2024-3919e1d89e", "fiscal_year": "FY2024", "use_case_name": "Artificial Intelligence-based Deduplication Algorithm for Classification of Duplicate Reports in the FDA Adverse Event Reports (FAERS)", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance", "product-safety" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "The deduplication algorithm is applied to all historical and incoming case report submissions to the FDA Adverse Event Reporting System (FAERS) to identify duplicate individual case safety reports (ICSRs). Unstructured data that is in free text FAERS narratives is processed through a natural language processing system to extract relevant clinical features. Both structured and unstructured data are then used in a probabilistic record linkage approach to identify duplicates. Application of the deduplication algorithm is optimized for processing entire FAERS database to support the data mining analytics process. Enhance the efficiency of removing duplicate FAERS reports to support data mining." }, { "tracker_id": "hhs-2024-5485a9b201", "fiscal_year": "FY2024", "use_case_name": "Development of automation tools and data warehouse to facilitate BE assessments", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Enhancing and streamlining the data entry associated with the review of bioequivalence data in Abbreviated New Drug Application (ANDA) submissions Reduce manual effort by preparing data for analysis and exporting results as part of the bioequivalence review. This tool will facilitate data collection and Information retrieval from the firm-submitted ANDA packages but will not involve reviewers' evaluation or judgement." }, { "tracker_id": "hhs-2024-a00f640ed3", "fiscal_year": "FY2024", "use_case_name": "DMF (Drug Master File) Facilites", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Extracts facility references from Type II (Drug Substance) DMF manufacturing submissions (i.e., DMF123456 discloses that it uses Facility X for manufacturing and Facility Y for stability testing). These DMF submissions may include structured documents (3938 form) or unstructured documents (eCTD module 3). This pipeline parses content from these documents, extracts facility references and supply chain roles (i.e., FEI 6789 – manufacturing facility), and exposes the data in a structured format for analysis. This allows OPQ to identify discrepancies between facilities reported on marketing applications and those disclosed by DMFs referenced by those applications. The extracted data are presented in a dashboard, along with additional data, for downstream human analysis. The ultimate purpose of this use case is to build a comprehensive inventory of facilities reported within DMF submissions as well as the applications that reference those DMFs." }, { "tracker_id": "hhs-2024-4c52b86e2f", "fiscal_year": "FY2024", "use_case_name": "Four Part Harmony Identification", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Accuracy of language used to report back deficiencies to submitters. This will reduce the amount of time it takes and reduce staff burden, thus allowing for MDUFA requirements to be met." }, { "tracker_id": "hhs-2024-eeea8d29bf", "fiscal_year": "FY2024", "use_case_name": "Information Visualization Platform (InfoViP) to Support Analysis of adverse event reports", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Information Visualization Platform (InfoVIP) supports post-market surveillance using AI to assist prioritization and analysis of adverse event reports by extracting data from unstructured case narratives and combining it with structured data to support analysis and review of adverse event reports. The tool assists with case series analyses, creating data visualization, and classifying adverse event reports by information quality. Improve the efficiency in preparing and analyzing data during the review and evaluation of adverse event reports." }, { "tracker_id": "hhs-2024-c972b9e77d", "fiscal_year": "FY2024", "use_case_name": "LLM-Assisted VAERS Analyses", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Build capacity for and assess the application of a LLM to VAERS (Vaccine Adverse Events Reporting System) to provide reviewers adhoc VAERS queries and efficiently generate customized query outputs Efficiently generate customized query outputs of VAERS queries for reviewers." }, { "tracker_id": "hhs-2024-55d36dab98", "fiscal_year": "FY2024", "use_case_name": "Pharmacovigilance machine learning to detect excess safety signal", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "product-safety" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Post-market adverse event reporting Using a computer algorithm assists FDA in finding \"needle in the haystack\" events that are difficult to uncover with manual review of reports" }, { "tracker_id": "hhs-2024-a4bfd6b1b0", "fiscal_year": "FY2024", "use_case_name": "Module 3 Facilties", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Objective of this use case was to identify and extract all drug manufacturing facilities reported within unstructured module 3 submissions from marketing applications to build an comprehensive inventory of drug facilities The extracted data are presented in a dashboard, along with additional data, for downstream human analysis." }, { "tracker_id": "hhs-2024-9a5ca4a119", "fiscal_year": "FY2024", "use_case_name": "OPPQ Policy Bot (AKA Policy and Guidance Documents Search)", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Helps policy staff in searching documents containing regulations and responding to inquiries, and other policy related work. In developing policy and responding to inquiries, OPPQ staff often need to reference previously published policy documents in order to ensure consistency. This AI-enabled tool will allow policy leads and other staff to quickly identify and query the appropriate documents." }, { "tracker_id": "hhs-2024-1d525b2548", "fiscal_year": "FY2024", "use_case_name": "Packaging Materials and Suppliers", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Objective of this use case aims is to extract data from unstructured sources to build an inventory of drug packaging materials and their suppliers to support staff on conducting drug supply chain analysis The extracted data are presented in a dashboard for downstream analysis." }, { "tracker_id": "hhs-2024-e96fa8a991", "fiscal_year": "FY2024", "use_case_name": "Process Large Amount of Submitted Docket Comments", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "To enhance the automatic process of dockets, we have created an AI/ML tool in CBER/HIVE that automatically download dockets and process them to accelerate the review of docket comments, significantly improving the efficiency and accuracy of our regulatory processes Efficiently generate customized query outputs of VAERS queries for reviewers." }, { "tracker_id": "hhs-2024-a92393914d", "fiscal_year": "FY2024", "use_case_name": "Real World Data/Evidence", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Identify industry unstructured submissions containing Real World Data/Evidence (RWD/E) by analyzing parsed content for likely indicators of Real World Data/Evidence RWD/E to support congressional reporting. The extracted data are presented in a dashboard for downstream human analysis." }, { "tracker_id": "hhs-2024-9d862eb565", "fiscal_year": "FY2024", "use_case_name": "Regulatory Starting Material", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Extract RSMs and their suppliers from unstructured module 3 industry submissions to create an inventory that will illuminate the upstream supply chain. The extracted data are presented in a dashboard for downstream analysis." }, { "tracker_id": "hhs-2024-4face9a2cd", "fiscal_year": "FY2024", "use_case_name": "Resource Capacity Planning", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Forecasting human drug review program submissions and corresponding FDA workload Improve the forecasting of human drug review program expected workload in order to adjust fee revenue and fees to fund resources needed to complete expected workload" }, { "tracker_id": "hhs-2024-305e4fd861", "fiscal_year": "FY2024", "use_case_name": "Supply Chain Resilience Program", "previous_name_reported": "", "bureau_as_reported": "FDA", "component": "FDA", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Emergency Management", "ai_type_as_reported": "Not reported", "public_health_functions": [ "emergency" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Forecasting demand of medical devices and supplies. Forecast demand for critical devices during a variety of scenarios (e.g. natural disaster, PHE)" }, { "tracker_id": "hhs-2024-98ac4f33c7", "fiscal_year": "FY2024", "use_case_name": "AI Audit Resolution Assistant", "previous_name_reported": "", "bureau_as_reported": "HRSA", "component": "HRSA", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The objective of this initiative is to reduce the time for HRSA auditors to review/resolve Single audits. HRSA has experienced an exponential increase in Single audits which is attributable in COVID funding attributable to the Provider Relief Fund programs. Success will be measured through the increase in the number of Single audits assigned to each auditor. e.g., improved technological capabilities should allow HRSA Auditors to more efficiently review and closeout Single audits." }, { "tracker_id": "hhs-2024-d9f202c541", "fiscal_year": "FY2024", "use_case_name": "Knowledge Navigator", "previous_name_reported": "", "bureau_as_reported": "HRSA", "component": "HRSA", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The objective is to develop an AI model that can answer detailed and complex questions about the key programmatic document, Application and Program Guidance (APG), that is issued annually before the application cycle opens. The PoC LLM has Applicant and Program Guidance (APG) documents for 10 loan repayments and scholarship programs. This will allow loan repayment and scholarship analysts and call center agents to better respond to public inquiries from applicants and participants. If successful the Knowledge Navigator could be made available to applicants and participants and reduce the volume of calls to the BHW Call Center along with the number of call center agents." }, { "tracker_id": "hhs-2024-baf33ad076", "fiscal_year": "FY2024", "use_case_name": "Policy Assistant", "previous_name_reported": "", "bureau_as_reported": "HRSA", "component": "HRSA", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "HRSA is looking forward to leveraging Generative AI tools to support and enhance the current drafting of several policy documents, funding opportunities, and notices. This could streamline the policy development process, reduce drafting time, and improve document quality by providing critical analysis and suggestions for improvement, thus enhancing the overall policy-making workflow. Reduce the amount of time required to develop first drafts of key BHW policy documents including APGs, NOFOs, budget documents" }, { "tracker_id": "hhs-2024-04e37a373f", "fiscal_year": "FY2024", "use_case_name": "Scholar Match", "previous_name_reported": "", "bureau_as_reported": "HRSA", "component": "HRSA", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The current candidate evaluations and placement process is complex and challenging for both analysts and participants. By enhancing this process with AI/ML support could optimize resource allocation and candidate satisfaction, significantly impacting workforce distribution and efficiency in critical health areas. This would improve the process of matching NHSC and Nurse Corps Scholars going into clinical service in underserved communities." }, { "tracker_id": "hhs-2024-c49b86e926", "fiscal_year": "FY2024", "use_case_name": "Scholarship Insight", "previous_name_reported": "", "bureau_as_reported": "HRSA", "component": "HRSA", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Rights-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The NHSC and Nurse Corps Scholarship reviewers need to receive and score thousands of essays submitted and the manual process delays the application processes. We are looking forward to building a Generative AI program that looks at all of the previous essays and how the scoring rubric was done and train it to do the first cut at scoring the essays. This could enhance the fairness and efficiency of scholarship evaluations, ensuring a thorough review process that supports equitable student opportunities. Reduce the amount of time required for internal or external reviews to evaluated NHSC and Nurse Corps scholarship applications and ensure that human reviewers are following the appropriate scoring rubric." }, { "tracker_id": "hhs-2024-b7e5bc146d", "fiscal_year": "FY2024", "use_case_name": "Site Application Analysis", "previous_name_reported": "", "bureau_as_reported": "HRSA", "component": "HRSA", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Rights-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The current application review process requires internal analysis to review many complex documents to determine Site Eligibility to employ a member of the NHSC or Nurse Corps. By enhancing this process with AI/ML support could revolutionize and streamline the NHSC, Substance Use Disorder Treatment and Recovery (STAR)and Nurse Corps Site Application Process. This would improve the current highly manual process for reviewing Site applications for internal analysts but ensure that clinical sites are being approved for NHSC, Nurse Corps, and STAR LRP." }, { "tracker_id": "hhs-2024-b1caa4eb43", "fiscal_year": "FY2024", "use_case_name": "A Cluster-Based Machine Learning Approach for Evaluating Factors Associated with Plasma Neurodegenerative Biomarkers", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This AI Use Case is Retired, No Longer in Use. This study investigated whether incorporating health-related risk factors can improve the predictive power of plasma biomarkers (related to neurodegeneration and inflammation) for incident dementia. Improve incident dementia prediction using plasma neurodegeneration biomarkers in a diverse population." }, { "tracker_id": "hhs-2024-92c82a0717", "fiscal_year": "FY2024", "use_case_name": "AccessGUDID Data Validation", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The artificial intelligence (AI) system for AccessGUDID will focus on enhancing data quality assurance by detecting anomalies in medical device metrics, such as inconsistent units of measurement or unusual device dimensions. It will analyze both structured data, like numerical measurements, and unstructured data, such as free text entries. The AI will not modify data directly but will flag potential issues for human review. Integrated into the U.S. Food and Drug Administration (FDA)'s processing workflow, it will leverage standard codes for precise grouping and employ advanced language models to process unstructured data. Deployed within the regulatory compliance and data management areas of NLM, this AI system aims to streamline data validation processes, reduce manual workload, and maintain the integrity of the AccessGUDID database, without overstepping into areas requiring human oversight. The key problem the AI aims to address is the presence of inconsistent or incorrect data entries, such as mismatched units of measurement or outlier device dimensions, which can lead to regulatory compliance issues and potential risks in device usage. By automating the anomaly detection process, the AI will improve the efficiency of data validation, significantly reducing the manual effort required to identify and correct errors. This will not only enhance the accuracy of the data but also speed up the processing and integration of new submissions into the AccessGUDID database. Improved data integrity will lead to better decision-making for manufacturers, healthcare providers, and regulators, ensuring that medical devices on the market meet safety and performance standards. Ultimately, the positive outcomes of this AI application include increased trust in the medical device information provided to end users, which contributes to the broader goal of improving public health and ensuring patient safety across the nation." }, { "tracker_id": "hhs-2024-8ab5220748", "fiscal_year": "FY2024", "use_case_name": "AI Solution for Image Duplication Detection", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NIA researchers generate a significant number of microscopic images for each publication, ranging from 100 to 200 TIFF files per study. Ensuring that these images are not duplicated or overlapped in any publications is crucial for maintaining research quality and credibility. The current manual verification process is time-consuming, prone to human error, and insufficient for handling large volumes of data. NIA researchers generate a significant number of microscopic images for each publication, ranging from 100 to 200 TIFF files per study. Ensuring that these images are not duplicated or overlapped in any publications is crucial for maintaining research quality and credibility." }, { "tracker_id": "hhs-2024-77f0df3f03", "fiscal_year": "FY2024", "use_case_name": "AI Solution for Research Funding Gap Analysis (RF-GAP)", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NIA is focusing on identifying and addressing funding and publication gaps in NIH-funded research. Using text-mining and artificial intelligence techniques on data from both NIH and external sources, NIA aims to uncover research areas that need more funding or lack sufficient publication coverage. This approach will help NIA leadership make informed decisions, refine funding strategies, and enhance the overall impact of NIH-funded research. Program officers will utilize a user-friendly dashboard for actionable insights, simplifying the tracking of research outputs and identification of gaps. 1. Improved allocation of NIH funding through targeted identification of underfunded research areas and initiatives to address publication gaps. 2. Enhanced integration of data sources will provide a comprehensive view of research trends. AI-driven analysis will offer actionable insights into emerging research areas and future funding needs. 3. Customized dashboards will empower program officers with efficient tools to monitor and manage research portfolios, fostering transparency and maximizing operational efficiency across NIA. This approach will help NIA leadership make informed decisions, refine funding strategies, and enhance the overall impact of NIH-funded research." }, { "tracker_id": "hhs-2024-c6be345678", "fiscal_year": "FY2024", "use_case_name": "AI/ML for Study Section Prediction in NICHD", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The project of AI/ML for Study Section Prediction is to classify the grant applications to specific Study Sections in the Scientific Review Branch in NICHD. It employs human-in-the-loop development which enables human evaluation and offers the ability to override and alter model outputs. Source data are from the NIH IMPAC II system via the eRA Review Module. The AI process and output data have no impact on the NIH IMPAC II system. The business area addressed is the alignment of appropriate Study Section for a given grant application to assist the Scientific Review Branch (SRB) at NICHD. This project adapts the methods and systems developed for the RPAB AI/ML Referral System project to optimize the assignment of grant applications to the most suitable study sections. The AI system will contribute to NIH's mission by improving the efficiency and accuracy of alignment of grant applications to study sections, ensuring that applications are reviewed by the most appropriate researchers, leading to scientific discoveries and health advancement." }, { "tracker_id": "hhs-2024-731cbd9a3d", "fiscal_year": "FY2024", "use_case_name": "AI-enabled landscape analysis of New Approach Methodologies (NAMs) in biomedical research literature", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Part of NIH Common Fund strategic planning efforts for new program. Complement Animal Research in Experimentation (Complement-ARIE) aims to identify opportunities and use trends for NAMs in biomedical research. Methods development in using genAI for literature searching and extraction applicable to other use cases. GenAI was used to survey the biomedical research literature to inform potential investment of resources and opportunities for the NIH Common Fund program. Methods are also applicable to other systematic literature review approaches." }, { "tracker_id": "hhs-2024-a1d2c9c8cc", "fiscal_year": "FY2024", "use_case_name": "Applying for Grants Chat Bot", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "A chatbot can significantly aid prospective grantees by providing instant, detailed information on specific grants, including eligibility criteria, application deadlines, and required documentation. It can guide users through the application process step-by-step, recommend additional resources for grant writing, and help determine eligibility to save time. Additionally, chatbots can assist in tracking application status, address common FAQs, and gather data on user needs. A chatbot streamlines the grant application process, reduces frustration, and enhances the overall experience for those seeking funding. It can guide users through the application process step-by-step, recommend additional resources for grant writing, and help determine eligibility to save time." }, { "tracker_id": "hhs-2024-f8e48bec46", "fiscal_year": "FY2024", "use_case_name": "Assessment of DTT/NTP research effectiveness", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Dextr/LaserAI does not use PII, PHI or demographic data. Dextr is FEDRAMP certified and ATO in the cloud. Data sources for Dextr are published PDFs accessible through open source publications (e.g. PubMed Central) or through our subscriptions (e.g., NLM). Modification and customization of the DEXTR automated extraction tool has been implemented to identify and capture Division of Translational Toxicology (DTT) and National Toxicology Program (NTP) research citations in the published literature, regulations, and popular press. The DEXTR automated workflow with user-verification supports the identification of these citations that will be used to assess impact and effectiveness of DTT and NTP research. Division Directors and Branch Chiefs require an evidence-based approach to assess the effectiveness of current and past research. Such an approach would be highly valuable for research prioritization." }, { "tracker_id": "hhs-2024-2692e8925a", "fiscal_year": "FY2024", "use_case_name": "Assisted Referral Tool", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Review activities of the Center for Scientific Review (CSR) are organized into Review Branches (RBs). Each RB represents a cluster of study sections around a general scientific area. Grant applications generally are assigned first to an RB, and then to a specific study section within that RB for evaluation of scientific merit. To provide assistance in assigning appropriate scientific areas for grant applications." }, { "tracker_id": "hhs-2024-1ba40455fb", "fiscal_year": "FY2024", "use_case_name": "Autism Spectrum Disorder (ASD) Classification Model for Children using Deep Neural Network", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This AI Use Case is Retired, No Longer in Use. The AI was used for evaluating whether a child around the age of 10 is exhibiting Autism Spectrum Disorder (ASD), and its severity. This AI Use Case is Retired, No Longer in Use. The primary objective of the AI use case was to enhance the accuracy and efficiency of diagnosing Autism Spectrum Disorder (ASD) by automatically categorizing ASD using deep learning models trained on Inertial Measurement Unit (IMU) hand tracking data. The AI aimed to address the complexity and variability in ASD diagnosis, which currently relies on subjective behavioral assessments." }, { "tracker_id": "hhs-2024-0c3973e0f8", "fiscal_year": "FY2024", "use_case_name": "Automated annotation of study data using knowledge organization systems", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Application to support automation of systematic review processes and annotation of extracted information from studies in the published literature. Automating the annotation of extracted information from environmental health studies will support joining with other structured datasets to serve as training data for predictive model development, validation of new approaches, and more robust analyses on weight of evidence of chemical effects on biological systems." }, { "tracker_id": "hhs-2024-ca0f6a2114", "fiscal_year": "FY2024", "use_case_name": "Automated approaches for table extraction", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Division of Translational Toxicology (DTT) is an intramural division at NIEHS. DTT's mission is to improve public health through data and knowledge development that are translatable, predictive, and timely. DTT scientists use a variety of traditional and cutting-edge approaches to better understand how factors in our environment may impact health. Automated approaches for identifying and extracting data from tables will save time and reduce level of effort for teams evaluating published studies (epidemiology reviews or meta-analyses, hazard assessments, etc.) and ultimately impacts development of health effect conclusions." }, { "tracker_id": "hhs-2024-7b5c09cc52", "fiscal_year": "FY2024", "use_case_name": "Automated Basic-Applied Categorization of extramural grants", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This machine-learning algorithm is in development. Currently it uses information on NIMH-funded extramural research projects to categorize them as basic or applied research per the federal definitions for each. The data it is trained on is supplied from extramural grant applications and the same fields will be used for subsequent classification of new grants. For new grants, the algorithm will propose a categorization which can be reviewed, confirmed, or edited by an NIMH staff member. The resulting information will be used for internal, analytical purposes and annual reporting of NIMH-funded basic and applied research. The AI will NOT influence funding decisions, require or use PII, or use any other sensitive, personal, or health information. The algorithm is intended to be consistent in identifying basic and applied research, reduce burden of review by NIMH staff, and provide a complementary perspective to human review." }, { "tracker_id": "hhs-2024-8a6ab4f064", "fiscal_year": "FY2024", "use_case_name": "Automated extraction of study methods and assessment of reliability", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "Application to support automation of systematic review processes and quality assessment of studies in the published literature (environmental health studies). This project is focused on automating the identification of studies with reliable/well established methods (e.g., corresponding to regulatory guidelines) from the literature. Supervised and unsupervised methods are being applied to a) extract the methods details from known studies, and b) identify studies whose methods fulfill reliability criteria. Identification and critical assessment of existing research is required for developing evidence-based conclusions, directing future research, and informing policy decisions by scientists, Branch Chiefs and Division Directors. Study reliability or quality are important considerations in the assessment of published research studies and are very time and labor intensive using manual approaches." }, { "tracker_id": "hhs-2024-2d0e4ed23f", "fiscal_year": "FY2024", "use_case_name": "Automation of Receipt & Referral Process of NIDDK grant applications", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This use case is centrally reported under OER's Internal Referral Module. NIDDK receives about 100 to 500 applications per week. NIDDK referral staff uses the IMPAC II QVR tool to stratify the applications between divisions based on a “Like Matrix” function to match the Division’s portfolios and the Title, Abstract, and specific aims of grant applications. This process is laborious and manual. Hence, automation was proposed. The scope of the effort is to provide a capability for NIDDK to analyze grant applications using NIDDK-specific business rules, complete the pilot, and the full implementation of the Artificial Intelligence (AI) and Natural Language Processing (NLP) tools for auto assignment of program class codes. NIDDK receives about 100 to 500 applications per week. The AI and machine learning tool will stratify the applications between programmatic divisions and Program Class Code (PCC) to match the Division’s portfolios based on the Title, Abstract, and specific aims of grant applications. It collectively saves NIDDK Program and Referral Staff 3000+ hours annually on Receipt and Referral activities. In addition, it instantly recommends PCC, allows Division designees to accept or change recommendations, tracks all grants that need PCC assignment, gives real-time information about the application status, and support business rules/generates reports." }, { "tracker_id": "hhs-2024-07c93882ed", "fiscal_year": "FY2024", "use_case_name": "Best Match: New relevance search for PubMed", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NLM is the world’s largest biomedical library. Best Match is a relevance search algorithm for NLM’s PubMed – a free search engine for biomedical literature accessed by millions of users around the world every day. Best Match increases the effectiveness of PubMed searches across the rapidly growing collection of biomedical literature to help users efficiently find the most relevant and high-quality information they need. PubMed comprises more than 37 million citations for biomedical literature from MEDLINE, life science journals, and online books." }, { "tracker_id": "hhs-2024-7e6b6379c6", "fiscal_year": "FY2024", "use_case_name": "Biomedical Citation Selector (BmCS)", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NLM is the world’s largest biomedical library. MEDLINE is NLM's bibliographic database that contains references to journal articles in life sciences, with a concentration on biomedicine. Through automation, NLM is able standardize article selection and reduce the amount of time it takes to process MEDLINE articles." }, { "tracker_id": "hhs-2024-342a2f09e9", "fiscal_year": "FY2024", "use_case_name": "Chatbot for researchers to find data sets for environmental health research efforts", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "This project is focused on the use of large language models and chatbot technology to provide a way for researchers to find data sets of relevance for environmental health research efforts. The primary objective is providing a tool that aids researchers in finding useful resources, specifically data sets, thus benefitting research efforts in finding the best data to use quickly." }, { "tracker_id": "hhs-2024-c4040f7232", "fiscal_year": "FY2024", "use_case_name": "Clinical Trial Predictor", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NIGMS supports basic research that increases our understanding of biological processes and lays the foundation for advances in disease diagnosis, treatment, and prevention. NIGMS-funded scientists investigate how living systems work at a range of levels from molecules and cells to tissues and organs, in research organisms, humans, and populations. The AI tool predicts whether grant applications may involve clinical trials based on the text of their titles, abstracts, narratives, specific aims, and research strategies." }, { "tracker_id": "hhs-2024-615fa5d665", "fiscal_year": "FY2024", "use_case_name": "ClinicalTrials.gov Protocol Registration and Results System Review Assistant", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "ClinicalTrials.gov is a website and online database of clinical research studies and information about their results. The purpose of ClinicalTrials.gov is to provide information about clinical research studies to the public, researchers, and health care professionals. This project aims to help ClinicalTrials.gov determine whether the addition of AI could make reviewing study records more efficient and effective." }, { "tracker_id": "hhs-2024-05ba1cbb4c", "fiscal_year": "FY2024", "use_case_name": "Collections Summarization Chatbot", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The artificial intelligence (AI) use case involves developing a chatbot that can answer contextual questions about collection objects that users are browsing within the NLM Digital Collections. The chatbot will not handle inquiries unrelated to collection objects or queries outside its defined scope. It utilizes the Retrieval-Augmented Generation (RAG) framework on Amazon Web Services (AWS) to generate summaries or detailed answers based on the content users are viewing. The chatbot interface will integrate with existing NLM web technologies, activating when a user spends an extended time on a resource page. The AI system will use text data from the collection objects and associated metadata to generate responses. The primary business area for deployment will be within NLM’s digital services, focusing on enhancing user interaction and accessibility. The main goal is to provide users with concise summaries and detailed answers about NLM Digital Collections." }, { "tracker_id": "hhs-2024-a421634e01", "fiscal_year": "FY2024", "use_case_name": "Computed Author: author name disambiguation for PubMed", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NLM is the world’s largest biomedical library. PubMed comprises more than 37 million citations for biomedical literature from MEDLINE, life science journals, and online books. NLM developed a machine-learning method to score the features for disambiguating a pair of papers with ambiguous names. Subsequently, agglomerative clustering is employed to collect all papers belonging to the same authors from those classified pairs. Disambiguation performance is evaluated with manual verification of random samples of pairs from clustering results, with a higher accuracy than other state-of-the-art methods. It has been integrated into PubMed to facilitate author name searches." }, { "tracker_id": "hhs-2024-eaf0cb625f", "fiscal_year": "FY2024", "use_case_name": "COVID-19 Pandemic Vulnerability Index Dashboard", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The dashboard creates risk profiles, called PVI scorecards, for every county in the United States, continuously updated with the latest data that summarize and visualize overall disease risk. Aims to reduce the impact of COVID-19 on vulnerable populations by aiding equitable decision-making on resource allocation and implementation of local- and state-level interventions. Available at https://covid19pvi.niehs.nih.gov/." }, { "tracker_id": "hhs-2024-fb1259956f", "fiscal_year": "FY2024", "use_case_name": "CSR Public Chatbot (CPC)", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This tool is a chatbot that answers questions from potential applicants and reviewers. Instead of providing lists of links to look through, the chatbot provides answers with links where the answers were drawn from. Source materials are limited to official government web pages, and a disclaimer is included cautioning the user to check the source materials to confirm the accuracy of the answers. This chatbot is intended to provide answers to the public about questions related to NIH peer review policies and procedures." }, { "tracker_id": "hhs-2024-1a48e22967", "fiscal_year": "FY2024", "use_case_name": "DAIT AIDS-Related Research Solution", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NIAID administers HIV/AIDS grant portfolios. The incoming grant applications are ranked based on these predictions and more highly-ranked applications are prioritized for review." }, { "tracker_id": "hhs-2024-43c9a503c7", "fiscal_year": "FY2024", "use_case_name": "Detecting Overlapping Science (DOS)", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This tool detects applications that represent potential duplicate funding (funding the same research in different projects). It examines applications as they are submitted to the NIH and sends a report to relevant personnel in the agency. Detect and prevent duplicate funding with real-time examination of incoming grant applications." }, { "tracker_id": "hhs-2024-c02d815efc", "fiscal_year": "FY2024", "use_case_name": "Detection of Implementation Science focus within incoming grant applications", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Grants management oversight and administration. Assign the grant application to a particular division for routine grants management oversight and administration." }, { "tracker_id": "hhs-2024-8110a267a2", "fiscal_year": "FY2024", "use_case_name": "DEXTR - automated data extraction tool", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Dextr/LaserAI does not use PII, PHI or demographic data. Dextr is FEDRAMP certified and ATO in the cloud. Data sources for Dextr are published PDFs accessible through open source publications (e.g. PubMed Central) or through our subscriptions (e.g., NLM). The primary objective of the DEXTR/Laser AI use case is to streamline the process of data extraction from published studies for literature reviews, addressing the key problem of time and labor intensity involved in manual data identification and capture. DEXTR leverages natural language processing (NLP) models, regular expressions and large language models to automatically identify and predict pertinent entities of data extraction within scientific studies. By doing so, it reduces the burden of manual extraction while still supporting user verification, offering a semi-automated approach that ensures accuracy and quality control. One of the key tasks DEXTR enhances is the annotation of studies during the normal workflow, allowing the tool to generate annotated corpora for the development of future models. Dextr also provides automated extraction of information from tables in PDFs expanding the supported data extraction capabilities. As a dynamic platform for multiple AI projects, DEXTR/LaserAI is continually being optimized and expanded to support a growing number of use cases. This automation not only accelerates data extraction but also aids in creating a robust, scalable system for handling the ever-increasing volume of published research. The anticipated positive outcomes include improved efficiency and accuracy in data extraction, which is critical for developing evidence-based conclusions, guiding future research directions, and informing policy decisions. By enabling faster and more comprehensive reviews of existing literature the tool has the potential to significantly enhance the ability to process large volumes of scientific data." }, { "tracker_id": "hhs-2024-97ba3aeb87", "fiscal_year": "FY2024", "use_case_name": "Division of International Services Customer Service Chatbot", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The initial version is designed to allow both foreign nationals and administrative officers (AOs) to interactively receive accurate answers to general questions about immigration policy and operations managed by NIH Division of International Services. The AI data store is compiled from DIS Frequently Asked questions and other relevant policy and general information. The benefit of the AI is to alleviate manual calls to the division for questions that can be covered more efficiently and accurately." }, { "tracker_id": "hhs-2024-8cca2928f0", "fiscal_year": "FY2024", "use_case_name": "Enhance NLM Website Accessibility", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NLM is pilot testing the use of artificial intelligence (AI) to enhance the accessibility of the NLM website in alignment with Section 508 standards. This initiative is critical to mitigate the enterprise risk associated with non-compliance with Section 508 standards covering web sites. The AI will focus solely on identifying and resolving accessibility errors associated with Section 508 standards for websites, including suggestions from the U.S. Web Design System framework when possible. The AI is currently being developed behind NIH's firewall for a select internal user base. The tool will not make direct changes to general web design or web content. The AI will integrate with existing web content management systems and accessibility compliance tools (like pa11y). The AI will use website data (HTML, CSS, JavaScript) and accessibility compliance standards as input and will be deployed in the web development and IT departments, particularly those focused on accessibility and compliance. The project may reduce costs and time associated with manual accessibility evaluation and remediation. Additionally, it is expected to enhance the accuracy and quality of accessibility features on the website. Importantly, the project aims to improve the user experience by making the site more accessible to a broader audience, thus increasing user access and discovery. Ultimately, this initiative will help reduce the enterprise risk associated with non-compliance and ensure that NLM's website meets the evolving standards for government web accessibility." }, { "tracker_id": "hhs-2024-361565cd70", "fiscal_year": "FY2024", "use_case_name": "Enhancing Responses to Customer Questions about NLM Products", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NLM is piloting the creation of an application which uses the GPT family of models from OpenAI (via Azure) and data retrieved from public facing NIH/NLM web domains to answer questions about select NLM products. This is currently being developed behind NIH's firewall for a select internal user base. The artificial intelligence (AI) will generate responses to questions but will not engage in direct conversations with customers. Additional safeguards would make sense in a production environment. The AI will integrate with a knowledge base and use text data from user technical questions and knowledge base articles. It will be deployed within customer service and technical support offices. Primary Objectives: Provide accurate and timely responses to technical questions about NLM products. Anticipated Positive Outcomes: Improved efficiency and accuracy in customer service, better user experience, and higher satisfaction rates." }, { "tracker_id": "hhs-2024-f49d55c3a8", "fiscal_year": "FY2024", "use_case_name": "Environmental health research annotation for model development", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [ "environmental-occupational" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "By creating annotated datasets that account for diverse study types, the project will enable the development of AI and machine-learning models that can significantly reduce the time and effort required to identify and capture critical data from environmental health studies. This improvement will streamline tasks for teams conducting literature assessments and reviews, improving their efficiency. The primary objective of this AI use case is to enhance the development of datasets specifically focused on toxicology and environmental health. The key problem it aims to address is the current scarcity of relevant training datasets or corpora, which limits the ability to create effective AI models for identifying and extracting environmental health data from published studies. This challenge is compounded by the diverse nature of environmental health research, which includes various study designs such as epidemiological studies, in vitro research, exposure assessments, and experimental animal studies." }, { "tracker_id": "hhs-2024-d565d68986", "fiscal_year": "FY2024", "use_case_name": "Expansion of Generative AI (GenAI) Caption Generation for all Collections Videos", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The artificial intelligence (AI) use case builds on an existing Generative AI (GenAI) workflow in NLM Digital Collections that generates transcripts and captions for all videos stored in the Digital Repository dark archive. The expansion of this workflow will apply to all videos and films targeted for Digital Collections. The AI will not be used for content outside of these collections or for non-video materials, as well as already captioned videos. It will integrate with existing video processing systems to automate the captioning process. The types of data utilized will include video files and associated metadata. The deployment will occur within NLM’s Digital Collections management area, with the primary goals being cost savings by reducing reliance on third-party vendors and standardizing video caption workflows across all collections. The objective is to expand AI-driven captioning across all videos in the NLM Digital Collections, ensuring standardized and accuracy." }, { "tracker_id": "hhs-2024-0b0a321849", "fiscal_year": "FY2024", "use_case_name": "Federal IT Acquisition Reform Act (FITARA) Tool", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NIAID administers contracts to execute mission initiatives. The tool's classifications inform SOW clarifications/revisions relevant to the FITARA." }, { "tracker_id": "hhs-2024-f4df68eb38", "fiscal_year": "FY2024", "use_case_name": "Generative AI (GenAI) Still Image Tagging", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The artificial intelligence (AI) use case will focus on identifying still images within the NLM Digital Collections that lack descriptions. This use case will employ Generative AI (GenAI) tools such as Amazon Web Services (AWS) Rekognition and/or AWS Bedrock to identify concepts and embedded text within these images. The AI system will not generate metadata for images that already have detailed descriptions or analyze images outside the NLM Digital Collections. The AI will connect with existing NLM metadata management systems, where the generated metadata will be stored and indexed. The primary types of data used will include image files from the digital collections and textual data derived from embedded text within those images. The AI system will be deployed within the NLM's business area, focusing specifically on enhancing the search and retrieval capabilities of the NLM Digital Collections. The use case aims to automatically generate accurate metadata for under-described images in the NLM Digital Collections, enhancing their searchability and discoverability. This improvement will increase access to valuable medical and scientific resources, supporting research and public health initiatives." }, { "tracker_id": "hhs-2024-38a522b647", "fiscal_year": "FY2024", "use_case_name": "Grant Application Subject-Matter Classification Tool", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The algorithm is used to recommend a specific scientific area within the Division's portfolio with a specific confidence level. This reduces the time it takes to assign a Program Class Code and personnel assignment to every grant application received by NIEHS. It searches for key words within the title, abstract and specific aims in each grant application to determine the best scientific category. It is not used in combination with any other data sets. It uses grant application data from IMPAC II, which is imported into the Grants Funding Decision Tool (a NIEHS Custom Product). Natural language processing of grant applications to assign scientific topics and corresponding Program Officer staff. The subject-matter classification tool improves the efficiency of the grant administration process by automatically assigning incoming applications a subject matter classification based on the text content of their abstracts using a model derived from past human-classified examples. This classification helps route each application to the program officer with the greatest expertise in the inferred field of study." }, { "tracker_id": "hhs-2024-a2edd1f55f", "fiscal_year": "FY2024", "use_case_name": "HIV-related grant classifier tool", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "A front-end application for scientific staff to input grant information, which then runs an automated algorithm to classify HIV-related grants. The tool allows OAR staff to determine the best categorization for a grant application after reviewing data and visualization of confidence levels through a heat map. Natural language processing of grant applications to assign scientific topics and corresponding Program Officer staff. The subject-matter classification tool improves the efficiency of the grant administration process by automatically assigning incoming applications a subject matter classification based on the text content of their abstracts using a model derived from past human-classified examples. This classification helps route each application to the program officer with the greatest expertise in the inferred field of study." }, { "tracker_id": "hhs-2024-6ca7ad2b09", "fiscal_year": "FY2024", "use_case_name": "Improving Customer Response", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NLM is pilot testing the utilization of artificial intelligence (AI) to assist Customer Service Representatives (CSRs) in drafting responses to customer inquiries. It is currently being developed behind NIH's firewall for a select internal user base. The AI is focused on generating draft responses for customer service but will not make final decisions or engage in direct communication with customers. CSRs are instructed to not enter personally identified info into the AI system. In the event this info is entered, the AI system is automatically prompted to not include any personally identified information in its generated response. Additional safeguards would make sense in a production environment or if it was released to the public. The AI will integrate with customer service platforms and databases like Medline Plus and DailyMed. The AI will use text data from customer inquiries and publicly accessible NLM-managed biomedical information services and be deployed within the customer service department. The project aims to enhance customer response metrics, increase customer satisfaction, and improve the efficiency of handling customer service inquiries." }, { "tracker_id": "hhs-2024-effc48ef1b", "fiscal_year": "FY2024", "use_case_name": "Improving Metadata Retrieval and Transformation for Metadata Management", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Dataset Catalog (catalog) is a catalog of biomedical datasets from various repositories for users to search, discover, retrieve, and connect with datasets to accelerate scientific research. Metadata from included repositories and their datasets are needed to appropriately index them in the catalog. This manual process can be labor intense, and thus NLM is pilot testing the development of a working interface for internal users to retrieve the metadata information from external repositories' websites and then transform the data to a specific format used in internal metadata management for the Dataset Catalog. This is currently being developed behind NIH's firewall for a select internal user base. The artificial intelligence (AI) will automate the retrieval and transformation of metadata but will not replace human oversight in critical metadata curation tasks. Additional safeguards would make sense in a production environment. The AI uses information from publicly available biomedical data repository websites and internal metadata management systems. The AI uses metadata from biomedical repositories and will be deployed within data management and IT departments, particularly those involved in cataloging and metadata management. Primary Objectives: Streamline metadata retrieval and transformation to improve efficiency and accuracy. Anticipated Positive Outcomes: Resource savings, enhanced data quality, and improved management of the Data Set Catalog." }, { "tracker_id": "hhs-2024-c99b0e63ca", "fiscal_year": "FY2024", "use_case_name": "Internal Referral Module", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The NIH Extramural Research Program uses the eRA system to efficiently award and manage grants. The AI system is designed for use by IC ‘internal referral’ officers who manage a pool of applications and are responsible for assigning the applications to program officials. It creates efficiencies by automating the ‘internal referral’ assignments, currently done manually on spreadsheets by the ICs. The inputs are grant applications, the outputs are referrals to Program Officers, Program Class Codes, Organizational units - Divisions and Branches and Scientific Research Clusters." }, { "tracker_id": "hhs-2024-cb57f3ab61", "fiscal_year": "FY2024", "use_case_name": "IT System Outage Alerts", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Use Generative AI to create maintenance and IT system outage alerts that will be sent to NIDCD staff. General communication to the NIDCD staff. The GenAI tool will reduce staff time to create and distribute e-mail alerts." }, { "tracker_id": "hhs-2024-45fdd1f64d", "fiscal_year": "FY2024", "use_case_name": "JIT Automated Calculator (JAC)", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NIGMS supports basic research that increases our understanding of biological processes and lays the foundation for advances in disease diagnosis, treatment, and prevention. NIGMS-funded scientists investigate how living systems work at a range of levels from molecules and cells to tissues and organs, in research organisms, humans, and populations. NIGMS needs to know how much total support an investigator has to ensure that it is not funding Principal Investigators who are already adequately resourced. The tool can determine how much funding a Principal Investigators has been awarded." }, { "tracker_id": "hhs-2024-5e1a1744eb", "fiscal_year": "FY2024", "use_case_name": "Large Language Automation", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Automate use cases as indicated below. 1. Automate BOT to locate and extract Resource Sharing Plan from post awarded grant application and feed into Microsoft Azure AI to teach it to determine if grant application as written has met compliance. The RFA provides guidelines for grantees. 2. Generate summary for published papers. 3. Generate summary for papers for concept development, research data analysis or other internal work. 4. Generate summary as part of a preliminary review of documents. 5. Generate summary of public meetings. 6. Utilize existing MS Excel worksheets to automate templated emails. 7. Generate FAQs. 8. Produce training materials. The RFA: 1) provides guidelines for grantees, 2) generates summary for published papers, 3) generates summary for papers for concept development, research data analysis or other internal work, 4) generates summary as part of a preliminary review of documents, 5) generates summary of public meetings, 6) utilizes existing MS Excel worksheets to automate templated emails, 7) generates FAQs, and 8) produces training materials." }, { "tracker_id": "hhs-2024-aec82e4379", "fiscal_year": "FY2024", "use_case_name": "Leveraging User-Generated Content for Digital Behavioral Interventions", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The project uses AI-based automated text classification technology to build a predictive model to categorize smoking-related tweets into six distinct categories (e.g., quitting strategies). Specifically, the project uses the AI-based BERT pre-trained language model, which will be further fine-tuned using neural network models to capture the contextual nuances of our use case. AI will also be used to select data for the training dataset that will form the basis of the predictive model. Then, we use ChatGPT to classify extracted and labelled tweets according to its sentiment, presence of sarcasm, presence of irony, presence of emojis. The primary objective of the project is to classify thousands of tweets to six categories (e.g., strategies for quitting, quitting benefits) and according to their sentiment and presence of irony, sarcasm, and emojis. Tweets that are positive, not ironic, nor sarcastic would then be tested with target audiences for message effectiveness and possible integration in a smoking cessation intervention." }, { "tracker_id": "hhs-2024-aeee2c5577", "fiscal_year": "FY2024", "use_case_name": "LLM Support for Admin Services", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This project aims to explore the integration of large language models (LLMs) into administrative operations, aiming to enhance efficiency, accuracy, and productivity. By leveraging LLMs, repetitive tasks can be automated including data analysis, documentation drafting, and customer support, thereby freeing up valuable time for administrative personnel to focus on more strategic initiatives. Additionally, LLMs can facilitate natural language processing (NLP) for documentation, ensuring consistency and accuracy in communication across various channels. Through process automation, policy monitoring, and virtual assistance, LLMs offer the potential to streamline our administrative workflow, reduce manual workload, and mitigate the risk of errors. This assessment will provide valuable insights into the feasibility and effectiveness of integrating LLMs into our administrative framework, ultimately optimizing our operations and enhancing organizational performance. It will reduce the burden of administrative tasks, faster response to administrative inquiries, fewer resources required to support administrative operations." }, { "tracker_id": "hhs-2024-f9a21924f6", "fiscal_year": "FY2024", "use_case_name": "Mapping Sequence Data to Research Outcomes", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "A proof-of-principle analysis is being conducted to identify downstream research products related to sequence records included in NLM-managed repositories. To identify sequence records in NLM-NCBI-managed data repositories and related research products to inform operations and prioritize resources." }, { "tracker_id": "hhs-2024-7226ce0d9d", "fiscal_year": "FY2024", "use_case_name": "Medical Text Indexer-NeXt Generation (MTIX) MEDLINE Indexing", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Machine learning-based system for the automated indexing of MEDLINE citation records using Medical Subject Heading (MeSH) terms. Automated indexing is achieved using a multi-stage neural text ranking approach. MeSH indexing has been shown to improve information retrieval in PubMed, but manual indexing was not sustainable due to the increasing volume of published biomedical literature. Automated indexing allows for cost-effective and timely indexing of MEDLINE citation records." }, { "tracker_id": "hhs-2024-ce68c80628", "fiscal_year": "FY2024", "use_case_name": "MetaMap", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NLM is the world’s largest biomedical library. MetaMap is a program providing access from biomedical text to the concepts in the Unified Medical Language System Metathesaurus. MetaMap uses NLP to provide a link between the text of biomedical literature and the knowledge." }, { "tracker_id": "hhs-2024-8e0e11b53f", "fiscal_year": "FY2024", "use_case_name": "MicroStrategy Evaluation", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The software to be evaluated should provide a business intelligence/reporting/visualization interface for possible use for 2 systems (Azure DevOps and OneStream). This software should provide robust development capabilities for manually-generated reports and AI capabilities for end-users to generate additional reports without the need for development. This software should provide robust development capabilities for manually-generated reports and AI capabilities for end-users to generate additional reports without the need for development." }, { "tracker_id": "hhs-2024-285f81b7b8", "fiscal_year": "FY2024", "use_case_name": "MTIX", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NLM is the world’s largest biomedical library. MEDLINE is NLM's bibliographic database that contains references to journal articles in life sciences, with a concentration on biomedicine. This is a machine learning-based system for the automated indexing of MEDLINE articles with Medical Subject Headings (MeSH) terms. Automated indexing is achieved using a multi-stage neural text ranking approach." }, { "tracker_id": "hhs-2024-d734f23226", "fiscal_year": "FY2024", "use_case_name": "NanCI: Connecting Scientists", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NCI is helping to connect scientists. NanCI is the app for biomedical scientists that helps discover scientific papers, build a career network, and explore events." }, { "tracker_id": "hhs-2024-ec9e3e8ee4", "fiscal_year": "FY2024", "use_case_name": "NBS Virtual Assistant", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The NIH Business System (NBS) is NIH's financial system. Streamlined Service Desk Operations. The AI-powered chatbot can handle routine user inquiries, reducing the burden on IT service desk. Provides self-service options for users to resolve issues independently." }, { "tracker_id": "hhs-2024-ec1653559f", "fiscal_year": "FY2024", "use_case_name": "NCI-DOE Collaboration, MOSSAIC project (Modeling Outcomes using Surveillance Data and Scalable AI for Cancer)", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [ "surveillance" ], "name_in_fy2024_inventory": "", "purpose_as_reported": "MOSSAIC applies deep learning natural language processing (NLP) and foundation models to population-based cancer data collected by NCI's Surveillance, Epidemiology, and End Results (SEER) program. DOE's Oak Ridge National Lab (ORNL) has data use agreements (DUAs) with multiple SEER registries to access and train models using SEER data. To date, the MOSSAIC pathology-coding API is deployed in 22 SEER registries, moving the US towards near real-time cancer incidence reporting. Other APIs are still in the validation phase or research and development phase. MOSSAIC enhances the infrastructure of the SEER cancer registries by providing tools that can increase the efficiency and accuracy of manual data abstraction by automatically extracting cancer surveillance data elements. SEER registries receive millions of unstructured clinical text documents that must be manually reviewed, leading to a lag in reporting of US cancer incidence trends. Automated tools such as those developed by MOSSAIC will help us achieve near real-time incidence trends and ultimately a more meaningful report card on the status of cancer in the US." }, { "tracker_id": "hhs-2024-78a2a342e0", "fiscal_year": "FY2024", "use_case_name": "NHLBI Chat", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NHLBI Chat is a secure LLM tooling providing access to the Azure OpenAI API so that all NHLBI staff can explore generative AI for their day-to-day need. NHLBI Chat is a secure LLM tooling providing access to the Azure OpenAI API so that all NHLBI staff can explore generative AI for their day-to-day need." }, { "tracker_id": "hhs-2024-09b2a74aef", "fiscal_year": "FY2024", "use_case_name": "NIAID GenAI Toolkit", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The NIAID GenAI Chat and Document Bot are Azure-hosted GenAI applications that leverage commercially available large language models from the Azure OpenAI Service to assist NIAID employees in a variety of task such as content generation, summarization, etc. The Azure-hosted NIAID GenAI Chat and Document Bot helps employees be more efficient with a wide variety of administrative tasks, such as summarizing/querying documents, drafting emails, and creating presentation outlines​, etc." }, { "tracker_id": "hhs-2024-882f6f92fa", "fiscal_year": "FY2024", "use_case_name": "NIAMS AI Chatbot Pilot", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "To develop a secured AI Chatbot environment where NIAMS staff can 1) conduct general research, 2) help staff in writing grants/publications/papers (e.g. foreign researcher with their English grammar, sentence structure, etc.), 3) scientific and IT code generation, 4) summarizing meeting minutes and 5) providing a platform to learn how AI can be used in their daily work tasks. Aside from what is described in 'Use Case Scope', other expected benefits are to assess the cost vs benefit value of having our own chatbot vs paying per user per month to use the NIH Enterprise versions of MS Copilot and OpenAI ChatGPT. This pilot will also be used to understand and develop a tiered business case on which NIAMS staff would fit which chatbot (NIAMS chatbot, MS copilot and OpenAI ChatGPT)." }, { "tracker_id": "hhs-2024-1fca618e03", "fiscal_year": "FY2024", "use_case_name": "NICHD RPAB AI/ML Application Referral System", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The NICHD RPAB AI/ML Application Referral System leverages the advanced AI/ML technologies to predict extramural branch assignments of new, incoming NICHD grant applications. It employs human-in-the-loop development which enables human evaluation and offers the ability to override and alter model outputs. Source data are from the NIH IMPAC II system and stored in the existing NICHD IT application CHIRP (Child Health Information Retrieval Program). The AI process and output data have no impact on the NIH IMPAC II system. The business area addressed is the internal referral assignment performed by RPAB. The primary objective is to enhance the efficiency, accuracy, and consistency of grant application referral assignments, while reducing the burden on Subject Matter Experts in RPAB. The AI system is expected to streamline the process of internal referral of new grant applications." }, { "tracker_id": "hhs-2024-4fc6749e76", "fiscal_year": "FY2024", "use_case_name": "NLM-Chem: towards automatic chemical indexing in PubMed articles", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NLM is the world’s largest biomedical library. MEDLINE is NLM's bibliographic database that contains references to journal articles in life sciences, with a concentration on biomedicine. Chemical indexing improves literature retrieval and information access. To assist this time-consuming and resource-intensive process, NLM developed NLM-Chem, an automatic tool for finding chemical names in the biomedical literature using advanced natural language processing and deep learning methods." }, { "tracker_id": "hhs-2024-c4ebec43e8", "fiscal_year": "FY2024", "use_case_name": "NLM-Gene: towards automatic gene indexing in PubMed articles", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NLM is the world’s largest biomedical library. MEDLINE is NLM's bibliographic database that contains references to journal articles in life sciences, with a concentration on biomedicine. Gene indexing improves literature retrieval and information access. To assist this time-consuming and resource-intensive process, NLM developed NLM-Gene, an automatic tool for finding gene names in the biomedical literature using advanced natural language processing and deep learning methods." }, { "tracker_id": "hhs-2024-d948a6dc04", "fiscal_year": "FY2024", "use_case_name": "NLP Automated Referral", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Publicly reported under OER. NIGMS worked with eRA for over a year to incorporate natural language processing (NLP) and machine learning (ML) algorithms into eRA's Internal Referral Module (IRM). IRM is now capable of using ML/NLP to assign applications directly to program officers (POs) based on the titles, abstracts, narratives, and specific aims of those applications, as well as the application histories of the PIs. The current process runs in the background on eRA's AWS cloud instances, and assigns the majority of new NIGMS applications to POs as soon as they are referred to NIGMS by CSR. ML/NLP is also used to provide POs with the three most relevant NIH institutions to applications to facilitate IC fit determinations. Automated referral allows NIGMS to retain institutional referral knowledge by training on historical data, eliminates delays in referral by assigning applications as soon as they come in, and reduces burden on staff members and allows them to allocate more of their time to other high value tasks." }, { "tracker_id": "hhs-2024-41f3ba6e27", "fiscal_year": "FY2024", "use_case_name": "OCIO GenAI Advisor", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The OCIO GenAI Advisor is a general productivity enhancement tool that uses Retrieval Augmented Generation (RAG) setup and user-provided data to answer questions about that data. This is a pilot project for experimentation to discover specific use cases, data needs, security and privacy concerns. If used effectively, the OCIO GenAI Advisor can shorten time to find the correct information and generate a non-trivial response for a complex question in a desired format such as security, architecture, PMO policy, reports." }, { "tracker_id": "hhs-2024-cffbcdd3a6", "fiscal_year": "FY2024", "use_case_name": "OIT Help Desk Chatbot", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "A general productivity enhancement tool that uses Retrieval Augmented Generation (RAG) setup to answer questions about that data. This is a proof of concept project for experimentation to discover specific use cases, data needs, security and privacy concerns. If used effectively, the OIT help desk can shorten resolution time for tickets, help empower users and generate a non-trivial response for a complex question in a desired format such as security, architecture, PMO policy, reports." }, { "tracker_id": "hhs-2024-244250ed5d", "fiscal_year": "FY2024", "use_case_name": "Person-level disambiguation for PubMed authors and NIH grant applicants", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The Office of Portfolio Analysis employs AI-based approaches to support analysis of the biomedical research landscape and inform data-driven decision-making by NIH leadership and extramural research administrators in other federal agencies. High-quality disambiguation is required to correctly link researchers to their grants and outputs including articles, patents, and clinical trials." }, { "tracker_id": "hhs-2024-db40045a64", "fiscal_year": "FY2024", "use_case_name": "Portfolio Analysis Summarization Tool (PAST)", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This AI Use Case is leveraging Power BI to present a dynamic view of grants within their portfolio, enabling program staff to explore their data interactively. Program staff face challenges in navigating and understanding extensive narratives within grants due to time constraints and the volume of information. The proposed Open AI sandbox will address these challenges by providing concise, AI-generated summaries of grant-related texts, including Titles, Abstracts, Specific Aims, and Research Strategies. Rapid summarization of custom research portfolios which will be used to support program staff and others across the institute." }, { "tracker_id": "hhs-2024-f2b9610270", "fiscal_year": "FY2024", "use_case_name": "Program Classification Coding", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "A machine learning model has been developed to provide Program Classification Code (PCC) suggestions to assist NIA Division Staff with their work. The model is trained using IMPAC II data and generates a report of the top three PCC suggestions for a selected council date. Users can filter the data view by IC/OrgCode and Program Officer names to further customize the results. The Division staff is responsible for handling a large volume of applications during each council round, with the task of processing and assigning PCCs. To aid users, a solution has been developed that suggests the specific PCC that should be assigned to each application." }, { "tracker_id": "hhs-2024-5657ff2217", "fiscal_year": "FY2024", "use_case_name": "RCDC AI Validation Tool", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Annually, NIH reports the support level for various research, condition, and disease categories based on grants, contracts, and other funding mechanisms used across the agency. To provide the public the transparency into the reporting of NIH's funded research." }, { "tracker_id": "hhs-2024-d69ecfbe27", "fiscal_year": "FY2024", "use_case_name": "Remediate Adobe .pdf documents to be more accessible", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Manual curation and remediation of PDF files is time-consuming and can require substantial expertise to remediate previously published artifacts. PDF documents should conformed to Section 508 accessibility standards. NLM is using AI to remediate Adobe .pdf files not currently accessible to Section 508 standards." }, { "tracker_id": "hhs-2024-2eec8ca772", "fiscal_year": "FY2024", "use_case_name": "Research Performance Progress Report (RPPR) Report Comparison", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The National Institute on Aging (NIA) handles a significant number of grant applications annually, requiring thorough review to prevent duplication, identify changes, and ensure optimal resource allocation. The current manual review process involves detailed comparisons across grant documents from different years, checking for overlaps with other projects, and summarizing extensive scientific text. With each Program Officer (PO) managing between 50 to 200+ grants and overseeing a total of $4 billion in grant funding, the process is highly time-consuming and prone to errors. Streamline the NIA grant application review process." }, { "tracker_id": "hhs-2024-af33923fe9", "fiscal_year": "FY2024", "use_case_name": "Scientific summaries tool", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The goal is to generate justifications for personnel actions including appointments, conversions, pay adjustments, and hiring/retention incentives for physicians, scientists, and veterinarians. Within NIAID DIR, we have team that drafts justifications for personnel actions based on the research being performed. This tool will be created to help them quickly and effectively prepare justifications for personnel actions for investigators in specific research fields." }, { "tracker_id": "hhs-2024-31c2356c20", "fiscal_year": "FY2024", "use_case_name": "Semantic group prediction in the UMLS Metathesaurus", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Developing AI approaches (neural networks, heuristics) to predict the semantic groups of new terms to be added to the Unified Medical Language System (UMLS) Metathesaurus. This is used during new concept integration into the UMLS Metathesaurus and is only intended to interact with the new concepts that will be added and existing UMLS Metathesaurus concepts. There is no training or intended use of this product for sensitive data. The hybrid system combining deep learning and heuristics should be able to effectively support UMLS editing and quality assurance. Support UMLS Metathesaurus editing. This should dramatically reduce the time needed to integrate new concepts into the UMLS Metathesaurus and increase the consistency." }, { "tracker_id": "hhs-2024-4f07e1f72f", "fiscal_year": "FY2024", "use_case_name": "Similarity-based Application and Investigator Matching (SAIM)", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NIGMS supports basic research that increases our understanding of biological processes and lays the foundation for advances in disease diagnosis, treatment, and prevention. NIGMS-funded scientists investigate how living systems work at a range of levels from molecules and cells to tissues and organs, in research organisms, humans, and populations. NIGMS needs to know how much total support an investigator has to ensure that it is not funding Principal Investigators who are already adequately resourced. The tool can determine if an application has significant overlap (Just-In-Time Other Support) with an application that is funded by another agency." }, { "tracker_id": "hhs-2024-f9f0ec8f54", "fiscal_year": "FY2024", "use_case_name": "SingleCite: Improving single citation search in PubMed", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NLM is the world’s largest biomedical library. SingleCite is search algorithm designed to improve single citation searches in the PubMed database. SingleCite helps increase the effectiveness of PubMed searches by making a user’s search for a specific document in PubMed more successful." }, { "tracker_id": "hhs-2024-610753b259", "fiscal_year": "FY2024", "use_case_name": "SRDMS NLP COI", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NIAID administers grant portfolios in the extramural research program areas including Allergy, Immunology, & Transplantation, Infectious Diseases, and HIV/AIDS. Detect individuals who may pose a conflict-of-interest during the grant review process." }, { "tracker_id": "hhs-2024-5c706c3b4e", "fiscal_year": "FY2024", "use_case_name": "Stem Cell Auto Coder", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "NIGMS supports basic research that increases our understanding of biological processes and lays the foundation for advances in disease diagnosis, treatment, and prevention. NIGMS-funded scientists investigate how living systems work at a range of levels from molecules and cells to tissues and organs, in research organisms, humans, and populations. Manually coding Stem Cell Research subcategories is a time intensive process. NIGMS sped up this process by using automation." }, { "tracker_id": "hhs-2024-954fb4d6b7", "fiscal_year": "FY2024", "use_case_name": "Study Section Clustering Tool (SSCT)", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This tool suggests how study sections might be organized into groups of similar scientific fields to decide how applications might be grouped into new study sections. Subject matter experts ultimately finalize the study section groupings. This tool helps to ensure that applications are grouped into study sections in a way that maps on to the current state of the science." }, { "tracker_id": "hhs-2024-ad7575412f", "fiscal_year": "FY2024", "use_case_name": "Synonymy prediction in the UMLS Metathesaurus", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Developing artificial intelligence (AI) approaches (neural networks) to predict synonymy among biomedical concepts in the Unified Medical Language System (UMLS) Metathesaurus, with application to the insertion of new concepts into the UMLS Metathesaurus. This is used during new concept integration into the UMLS Metathesaurus and is only intended to interact with the new concepts that will be added and existing UMLS Metathesaurus concepts. There is no training or intended use of this product for sensitive data. Preliminary results indicate the performance of the system should be sufficient to support the insertion of new terms into the UMLS Metathesaurus. Support UMLS Metathesaurus editing. This should dramatically reduce the time needed to integrate new concepts into the UMLS Metathesaurus and increase the consistency." }, { "tracker_id": "hhs-2024-2ea8eb7e18", "fiscal_year": "FY2024", "use_case_name": "TB Case Browser Image Text Detection", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The NIAID TB Portals Program is a multi-national collaboration for tuberculosis (TB) data sharing and analysis to advance TB research. A tool that uses AWS Recognition managed AI service to detect text in images which could be potentially Personally Identifiable Information (PII)/ Protected Health Information (PHI) in TB Portals data." }, { "tracker_id": "hhs-2024-5ca6ee0ef0", "fiscal_year": "FY2024", "use_case_name": "Tech Support Chatbot", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "It is designed to address two usage scenarios: (1) provide the ORS/ORF community with an interactive customer service chat interface for inquiries related to OIIT technology services, policy, and general information and (2) provide OIIT staff with the ability to receive answers in a variety of technical areas as they troubleshoot or manage efforts in their respective teams. The benefit of the AI is to improve administrative questions and efficiency of the information to the internal OIIT team." }, { "tracker_id": "hhs-2024-a96cda8ac4", "fiscal_year": "FY2024", "use_case_name": "Tool for PO Lookup Assignment (TPAL)", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Similar to publicly reported IRM use case. The Tool for PO Assignment Lookup (TPAL) provides NIGMS Program staff with the ability to automatically search for appropriate Program Officers (POs), including their corresponding Program Area Codes (PACs), and NIH ICs to assign or reassign project proposals, or to provide advice to prospective applicants. TPAL uses natural language processing and machine learning to predict the three most relevant NIGMS POs, including their corresponding PACs, and the three most relevant NIH ICs from the project description/abstract text entered into a textbox. Note that because the PO and IC prediction algorithms are independent of each other, TPAL will always try to provide the most relevant program​ officers, even when NIGMS is not one of the top three recommended NIH ICs. There are many occasions in which Division Directors, Branch Chiefs, and Program Officers wish to receive suggestions for the most appropriate people to talk to about a project proposal or where to send a proposal that might not be appropriate for NIGMS." }, { "tracker_id": "hhs-2024-53707c54ec", "fiscal_year": "FY2024", "use_case_name": "Transformative Research Award Anonymization Check (TRAAC)", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "This tool helps to determine whether grant applications for NIH Director's Transformative Research Awards are properly anonymized by quickly detecting information that might identify the applicant in the Specific Aims or Research Strategy sections. This tool automatically screens many pages and highlights any text that might allow identification of the applicants. The highlighted text is then examined by subject matter experts. This streamlines content screening and aids both NIH staff and the external scientific community in making more rapid and efficient determinations about whether anonymity has been compromised." }, { "tracker_id": "hhs-2024-cb8bc1f36b", "fiscal_year": "FY2024", "use_case_name": "Machine learning pipeline for mining citations from full-text scientific articles", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "The NIH Office of Portfolio Analysis developed a machine learning pipeline to identify scientific articles that are freely available on the internet and do not require an institutional library subscription to access. The pipeline harvests full-text pdfs, converts them to xml, and uses a Long Short-Term Memory (LSTM) recurrent neural network model that discriminates between reference text and other text in the scientific article. The LSTM-identified references are then passed through our Citation Resolution Service. For more information see the publication describing this pipeline: Hutchins et al 2019 (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000385#sec003). Retired use case" }, { "tracker_id": "hhs-2024-ec9f344c1d", "fiscal_year": "FY2024", "use_case_name": "Machine learning system to predict translational progress in biomedical research", "previous_name_reported": "", "bureau_as_reported": "NIH", "component": "NIH", "stage_as_reported": "Retired", "stage_fy2025_equivalent": "Retired", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "A machine learning system that detects whether a research paper is likely to be cited by a future clinical trial or guideline. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community’s early reaction to a paper. For more information see the publication describing this system: Hutchins et al 2019 (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000416) Retired use case" }, { "tracker_id": "hhs-2024-ed989efbb2", "fiscal_year": "FY2024", "use_case_name": "Assessing developing possible version of \"HHSGPT\" for OIDP-specific internal use", "previous_name_reported": "", "bureau_as_reported": "OASH", "component": "Office of the Secretary", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "OIDP's HIV.gov: an exploration of possibly using a custom knowledge-based walled/isolated version of \"Chat GPT\" for our internal OIDP users to access information to help with internal operational efficiencies related to our work on national strategies. We have been meeting for almost 1 year with senior HHS officials working on AI to keep them up to date on our approach and efforts in both RAG LLM and generative AI. We describe our work as an internal library. The tool is called Ask ABE! (v3.0.0-alpha.1) - AI-Driven. Beneficial. Equitable (ABE). It's like having a digital librarian who can help us at OIDP easily access and use all the valuable resources from HIV.gov to support our work in ending new HIV transmissions! Our work is critical to ensuring the following issues are addressed: (1) Security: FedRAMP certified Azure Open AI 3.5 Turbo / 4o Global. (2) Privacy: Not tracking conversations nor usage. PII stripped. (3) Equity: HIV Language and HIV Language Guideline through internal system prompt. (4) Hallucinations: Internal system prompt - ABE HIV.gov knowledge base and conversation history. There is a low risk of this occurring compared to open systems like ChatGPT. (5) Risk: NIST AI Risk Management Framework: Govern, Map, Measure, Manage for Responsible AI, Observable AI, and Explainable AI (6) Certifications / Training / Support: Azure AI Certifications / Data Science DoJo / Corporate Thought Leadership Support: Responsible AI Framework." }, { "tracker_id": "hhs-2024-75fa5f7dec", "fiscal_year": "FY2024", "use_case_name": "Informal generative AI research for OIDP's HIV.gov", "previous_name_reported": "", "bureau_as_reported": "OASH", "component": "Office of the Secretary", "stage_as_reported": "Implementation and Assessment", "stage_fy2025_equivalent": "Pilot", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Education & Workforce", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "OIDP's HIV.gov monitors (1) new and emerging AI tools/policies, generative AI use in the field of communications, and (2) the impact of generative AI on HIV federal content delivery through major search engines. This work is done by an ORISE fellow in conjunction with the HIV.gov communications team. The information is synthesized and discussed during a weekly communications meeting to determine any possible application or if additional follow-up is needed. The purpose of this activity is to determine potential tools that we may be able to use to extend the reach of our work. For example, we have been monitoring tools that potentially can synthesize weekly content into a short narrated video." }, { "tracker_id": "hhs-2024-d4f2ceadc5", "fiscal_year": "FY2024", "use_case_name": "Presenting/providing technical assistance on HIV.gov's cautious & transparent use of generative AI", "previous_name_reported": "", "bureau_as_reported": "OASH", "component": "Office of the Secretary", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Education & Workforce", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "OIDP's HIV.gov hosts generative AI workshops, 1-on-1 trainings, and limited generative AI labs to share our cautious and transparent use of generative AI to create communications content. These activities also include information on generative AI basics and information on federal guidance. Our limited AI-generated content is disclosed with a watermark or a tag saying, \"Created by AI, Reviewed By Humans.\" These trainings have occurred at 5 national HIV-related conferences and include multiple 1-1 training sessions and workshops that reinforce the importance of following best practices, being transparent, and being cautious. We have trained over 500 people on the cautious and transparent use of generative AI." }, { "tracker_id": "hhs-2024-f78bfa87b8", "fiscal_year": "FY2024", "use_case_name": "Utilizing HHSGPT Pilot for writing/editing", "previous_name_reported": "", "bureau_as_reported": "OASH", "component": "Office of the Secretary", "stage_as_reported": "Operation and Maintenance", "stage_fy2025_equivalent": "Deployed", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "Limited OIDP staff have been selected to have access to HHSGPT, an internal generative AI tool in its pilot phase. Team members have been using it for editing, brainstorming, idea generating, drafting text, revisions, gap analysis, organization/structure, evaluations, etc. HHSGPT has been used to assess national strategies, such as the 2026-2030 vaccine national strategy, the 2026-2030 viral hepatitis strategy, and others. When utilizing HHSGPT, we work to report back how this use has reduced labor burden and note any interesting findings when using the tool. Use reporting is done on an Excel file." }, { "tracker_id": "hhs-2024-69a4f46a7f", "fiscal_year": "FY2024", "use_case_name": "HHSGPT", "previous_name_reported": "", "bureau_as_reported": "OCIO", "component": "Office of the Secretary", "stage_as_reported": "Acquisition and/or Development", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "ChatGPT is an AI-powered chatbot developed by OpenAI, and it can be integrated with Azure, Microsoft's cloud computing platform. ChatGPT leverages the power of OpenAI's language model to provide natural language understanding and generation capabilities. Scalability: As an AI-powered chatbot, ChatGPT can handle multiple conversations simultaneously, scaling up to meet high demand without the need for additional human resources. Increased Efficiency: ChatGPT can automate repetitive tasks, freeing up human agents to focus on more complex or value-added activities, improving overall efficiency. Persona Benefits: Assist data scientists in data exploration, model prototyping, analysis, collaboration, and accessing relevant knowledge, enhancing their productivity and efficiency. Assist policy analysts by providing instant insights, answering complex policy-related questions, and facilitating interactive discussions, enabling faster and more informed decision-making. Assist in writing large policy documents by providing suggestions, generating coherent content, and offering language refinement, streamlining the writing process and ensuring high-quality outputs." }, { "tracker_id": "hhs-2024-e6a8a1d45d", "fiscal_year": "FY2024", "use_case_name": "DECIDE", "previous_name_reported": "", "bureau_as_reported": "SAMHSA", "component": "SAMHSA", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Health & Medical", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "At present, SAMHSA's Office of Behavioral Health Equity (OBHE) manually reviews grantee organizations' progress towards achieving their health disparity reduction goals. This process involves manually extracting Disparity Impact Statements, annual reports, enrollment data, and external population data to verify the accuracy of grantees' reported progress. This is a time-consuming process, thus the development of an AI-based tool for doing this review is in development. DECIDE will reduce the administrative burden on OBHE staff and allow them to provide feedback to grantees more quickly." }, { "tracker_id": "hhs-2024-3de4904021", "fiscal_year": "FY2024", "use_case_name": "Document drafting and editing", "previous_name_reported": "", "bureau_as_reported": "SAMHSA", "component": "SAMHSA", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "SAMHSA employees routinely write internal reports, public-facing written products, and other documents. Some parts of the writing process, such as structuring thoughts into an outline and proofreading, can be enhanced through the use of AI. The document drafting and editing tool will increase employee productivity and help produce polished written work products." }, { "tracker_id": "hhs-2024-1fe49073c7", "fiscal_year": "FY2024", "use_case_name": "Document summarization", "previous_name_reported": "", "bureau_as_reported": "SAMHSA", "component": "SAMHSA", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Mission-Enabling (internal agency support)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "SAMHSA employees routinely read and summarize large amounts of documentation for grantmaking, management, and other day-to-day work. Ingesting and condensing such vast amounts of information can take time away from SAMHSA's most important mission-driven work. The document summarization tool will expedite day-to-day workflows and help employees ingest large amounts of information more quickly." }, { "tracker_id": "hhs-2024-31522d2a76", "fiscal_year": "FY2024", "use_case_name": "Smart Search", "previous_name_reported": "", "bureau_as_reported": "SAMHSA", "component": "SAMHSA", "stage_as_reported": "Initiated", "stage_fy2025_equivalent": "Pre-deployment", "impact_designation": "Neither rights- nor safety-impacting", "topic_area_as_reported": "Government Services (includes Benefits and Service Delivery)", "ai_type_as_reported": "Not reported", "public_health_functions": [], "name_in_fy2024_inventory": "", "purpose_as_reported": "SAMHSA.gov team would like to procure \"Kendra\" to implement a smart search on store.samhsa.gov—improving access to the Federal Government’s leading source of behavioral health information. Currently the search on store.samhsa.gov relies on key words pulled from document titles, descriptions, and manually generated tags. Both anecdotal evidence and results of usability testing have demonstrated that the Store’s search capability is the single greatest barrier to users accessing materials they need Kendra will allow for scanning of all documents in the store to respond more accurately to consumer queries while providing a faster search." } ] }