Policy & regulation
Mello and Nagireddy on phased AI for Medicaid work-requirement eligibility
AI Policy and Governance adds a subsection on AI for Medicaid eligibility redetermination under the 2025 budget reconciliation law's new community-engagement (work) rules, citing a JAMA Health Forum argument for introducing AI in phases rather than all at once.
Why Addresses a live implementation question created by HR 1's new Medicaid work-requirement rules for state eligibility redeterminations.
Chapter AI Policy and Governance in Healthcare
New evidence
Banerjee and Nayak scenario cost model for autism identification
Diagnostic and Clinical Decision Support adds a calibrated U.S. scenario cost-of-illness model for autism identification delayed by specialist evaluation queues, explicitly framed as a projection exercise rather than an observed national savings figure.
Why Extends the chapter's bottleneck-and-downstream-action rule to non-imaging pathways, where specialist evaluation queues delay identification.
Chapter AI in Diagnostic and Clinical Decision Support
New evidence
Silva et al. comparison of CatBoost and GRU dengue forecasts across Brazil
Epidemic Forecasting with AI adds a 2026 International Journal of Biometeorology walk-forward comparison of CatBoost and GRU dengue-forecasting models across all 27 Brazilian state capitals, extending the chapter's point that multimodel forecasting patterns from COVID-19 also apply to dengue.
Why Gives the LMIC forecasting discussion a Brazil-specific model comparison alongside its existing dengue-challenge citations.
Chapter Epidemic Forecasting with AI
New evidence
Huang et al. evaluation of free-tier chatbots on HIV/AIDS FAQs
LLMs in Public Health adds a peer-reviewed evaluation in which three infectious-disease specialists scored free-tier ChatGPT-3.5 and DeepSeek-R1 (search off, default settings) on 26 common HIV/AIDS questions.
Why Fills a gap in the chapter's own evidence hierarchy for free-tier chatbot accuracy on population health information seeking.
Chapter Large Language Models in Public Health: Theory and Practice
New evidence
Abdul Rahman and Tun climate-scenario model of global disease burden
Epidemic Forecasting with AI adds a PLOS One climate-health scenario example: a GBD, CMIP6, and ERA5 simulation projecting global disease burden at 1.5, 2.0, and 2.5 degrees Celsius of warming, with a Random Forest R² of 0.89.
Why The paper's own 'AI-forecast' label describes a scenario simulation, illustrating the chapter's distinction between forecasts and projections in a climate-health example.
Chapter Epidemic Forecasting with AI
New evidence
Dominikowski et al. and Janvier et al. on gray-market performance-enhancing peptides
AI, Misinformation, and the Infodemic adds a parallel visual-misinformation failure mode: gray-market injectable peptides marketed as 'research compounds' and dosed from online protocols, citing a 2026 Frontiers in Endocrinology review that maps GH-IGF-1-axis performance-enhancing peptides.
Why Extends the misinformation chapter's visual and product-image failure modes to gray-market peptides sold and dosed outside clinical oversight.
Chapter AI, Health Misinformation, and the Infodemic
New evidence
Praschk et al. audit of LLM vaccine-stance classification
AI, Misinformation, and the Infodemic adds a zero-shot audit of two reasoning LLMs classifying vaccine stance on 3,060 rehydrated 2020-21 X posts: o4-mini reached 0.819 accuracy and 0.820 macro-F1, and Gemini 2.5 Flash reached 0.799 accuracy and 0.800 macro-F1.
Why Gives the misinformation chapter a measured accuracy audit rather than a deployed vaccine-stance classification system.
Chapter AI, Health Misinformation, and the Infodemic
New evidence
Qing et al. on VAERS/FAERS signals as a vaccine-safety evaluation control
Evaluating AI Systems for Healthcare adds an evaluation control: do not treat VAERS/FAERS disproportionality analysis combined with in-sample machine learning as incidence, causality, or a vaccine-safety endpoint. The cited study analyzed 9,446 AVA reports and found 180 depression-related cases.
Why Guards against reading VAERS/FAERS signals as causal vaccine-safety findings.
Chapter Evaluating AI Systems for Healthcare
New evidence
Saleem Al-Ansary et al. XGBoost classifier for foodborne outbreak detection
Disease Surveillance and Outbreak Detection adds a Frontiers in Public Health case study that trained an XGBoost outbreak classifier on 61 cleaned Eastern Province foodborne events, illustrating the base-rate problem with real records rather than hypothetical weekly figures; the classifier is described as a proof of concept, not an outbreak early-warning system.
Why Gives the base-rate-problem discussion a real-world foodborne surveillance example instead of a hypothetical weekly calculation.
Chapter AI in Disease Surveillance and Outbreak Detection
New evidence
Jiang et al. ensemble model for school-based vision screening
Diagnostic and Clinical Decision Support adds a nested cross-validated ensemble model trained on 8,035 Guangdong children aged 5 to 10 in a school vision screening program, framed as a different kind of 'early warning' claim than outbreak detection.
Why Distinguishes school-based screening performance from outbreak early-warning system performance, a distinction the chapter draws repeatedly.
Chapter AI in Diagnostic and Clinical Decision Support
New evidence
Fuad, Milki, and Aziz on dengue forecasting complexity in Bangladesh
Epidemic Forecasting with AI adds a PLOS One panel study across 11 Bangladesh districts illustrating that greater model complexity does not equal greater forecasting skill, alongside a note that a review of 67 AI-based early-warning studies found no pooled sensitivity benchmark across systems.
Why Reinforces the point that forecasting-model complexity does not guarantee skill, adding a Bangladesh dengue example alongside the chapter's broader early-warning evidence gap.
Chapter Epidemic Forecasting with AI
New evidence
Xiang et al. on symptom-based pathogen prediction for home testing
Disease Surveillance and Outbreak Detection adds a PLOS Digital Health home-testing study asking whether 13 self-reported symptoms can identify the likely pathogen well enough to prioritize multiplex PCR testing, distinguishing syndrome-level surveillance such as ILINet from pathogen-specific detection.
Why Clarifies that ILINet-style syndromic surveillance identifies a syndrome, not a specific pathogen, a distinction relevant to home-testing triage design.
Chapter AI in Disease Surveillance and Outbreak Detection
New evidence
Haq et al. analysis of E. coli contamination in Bangladesh drinking water
Disease Surveillance and Outbreak Detection adds a PLOS One analysis of 6,069 Bangladesh MICS 2019 household drinking-water samples, finding E. coli contamination in 39.2% of samples (95% CI 37.4 to 41.2) with geographic clustering by ward.
Why Gives the surveillance chapter a moderate-prevalence example of a data-verification rule, using household water-quality rather than disease case data.
Chapter AI in Disease Surveillance and Outbreak Detection
New evidence
Kim et al. on survey weighting bias in NHANES-based AI models
The Data Problem in Public Health AI adds a Frontiers in Digital Health NHANES methods paper analyzing 6,024 training and 1,926 test adults (2007 to 2014 cycles), giving the chapter's population-inference rule an empirical example based on unweighted versus weight-aware model performance.
Why Gives the chapter's population-inference rule an empirical NHANES example, following the same rule's earlier theoretical form.
Chapter The Data Problem in Public Health AI
New evidence
Stürenburg et al. classification of global health aid by disease burden
AI in Global Health and Equity adds a study using Llama-3.1-70B to classify 3.7 million OECD Creditor Reporting System project descriptions (about USD 332 billion, 157 countries, 2000 to 2022) into 17 Global Burden of Disease categories.
Why Extends the chapter's aid-versus-burden mismatch discussion beyond workforce capacity to how development aid funding is distributed across disease categories.
Chapter AI in Global Health and Equity
New evidence
Leibovitch et al. stage 1 DECIDE-AI evaluation of the SHAKED CDSS
Evaluating AI Systems for Healthcare adds a Nature Medicine DECIDE-AI stage 1 evaluation of SHAKED, a multi-LLM clinical decision-support system tested in a tertiary emergency department over 4 weeks with 1,138 patients, one wing using the system and one following routine rotations.
Why Gives the evaluation chapter a DECIDE-AI-framework example of early-stage clinical evaluation for a multi-LLM decision-support system.
Chapter Evaluating AI Systems for Healthcare
New content
Mahmood and Topol on tokenising population health journeys
Large Language Models in Public Health: Theory and Practice adds a new section on tokenising population health journeys, describing how Mahmood and Topol translate recorded health data (codes, measurements, text, images, and time intervals) into sequenced tokens that support population-scale prediction.
Why Introduces the concept of translating longitudinal health records into token sequences, extending the chapter's tokenization teaching from single words to population health journeys.
Chapter Large Language Models in Public Health: Theory and Practice
Revisions
Google AI Overview compared with specialized clinical evidence tools
Diagnostic and Clinical Decision Support notes that public search answers such as Google AI Overview can match specialized clinical evidence tools on some clinician-query benchmarks, while not substituting for clinical care or institutional decision support.
Why Extends the chapter's patient-facing safeguards to general-purpose search answers, not only dedicated chatbots or clinical decision-support tools.
Chapter AI in Diagnostic and Clinical Decision Support