Appendix O — Bibliography

Sources are listed in chapter order. Follow Cited in to return to the source’s context. Recommended reading is listed separately.

Entries retain the citation label when publication details are incomplete. Inclusion in this bibliography does not establish that a source supports every claim made about it.

Executive Summary

References

• Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travelIsaac I Bogoch; Alexander Watts; Andrea Thomas-Bachli; et al. 2020. Journal of Travel Medicinejournal articleCited in: Executive Summary
• Disparities in dermatology AI performance on a diverse, curated clinical image setRoxana Daneshjou; Kailas Vodrahalli; Roberto A. Novoa; et al. 2022. Science Advancesjournal articleCited in: Executive Summary
• FDA CDS FAQsCited in: Executive Summary
• FDA CDS Guidance, Jan 2026Cited in: Executive Summary
• Detecting influenza epidemics using search engine query dataJeremy Ginsberg; Matthew H. Mohebbi; Rajan S. Patel; et al. 2009. Naturejournal articleCited in: Executive Summary
• The Parable of Google Flu: Traps in Big Data AnalysisDavid Lazer; Ryan Kennedy; Gary King; et al. 2014. Sciencejournal articleCited in: Executive Summary
• PDFCited in: Executive Summary
• Wong et al., 2021Cited in: Executive Summary
• Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional studyJohn R. Zech; Marcus A. Badgeley; Manway Liu; et al. 2018. PLOS Medicinejournal articleCited in: Executive Summary

Part I: Foundations

AI in Healthcare: A Brief History

References

• Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: AI in Healthcare: A Brief History
• AlexNetCited in: AI in Healthcare: A Brief History
• AlphaFold (2021)Cited in: AI in Healthcare: A Brief History
• AlphaFold 1 (2020)Cited in: AI in Healthcare: A Brief History
• AlphaGo defeated the world Go championCited in: AI in Healthcare: A Brief History
• BERTCited in: AI in Healthcare: A Brief History
• ChestX-ray8 datasetCited in: AI in Healthcare: A Brief History
• Comparative studiesCited in: AI in Healthcare: A Brief History
• DeepVariant (2018)Cited in: AI in Healthcare: A Brief History
• DENDRAL (1965)Cited in: AI in Healthcare: A Brief History
• DXplainCited in: AI in Healthcare: A Brief History
• EARS (Early Aberration Reporting System)Cited in: AI in Healthcare: A Brief History
• Dermatologist-level classification of skin cancer with deep neural networksAndre Esteva; Brett Kuprel; Roberto A. Novoa; et al. 2017. Naturejournal articleCited in: AI in Healthcare: A Brief History
• FDA AI-Enabled Medical Device ListCited in: AI in Healthcare: A Brief History (passage 1); AI in Healthcare: A Brief History (passage 2)
• FDA DEN180001Cited in: AI in Healthcare: A Brief History
• formally born at Dartmouth CollegeCited in: AI in Healthcare: A Brief History
• Framingham Risk ScoreCited in: AI in Healthcare: A Brief History
• Detecting influenza epidemics using search engine query dataJeremy Ginsberg; Matthew H. Mohebbi; Rajan S. Patel; et al. 2009. Naturejournal articleCited in: AI in Healthcare: A Brief History (passage 1); AI in Healthcare: A Brief History (passage 2)
• Google, 2008Cited in: AI in Healthcare: A Brief History
• Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus PhotographsVarun Gulshan; Lily Peng; Marc Coram; et al. 2016. JAMAjournal articleCited in: AI in Healthcare: A Brief History
• HealthMap (launched 2006; Brownstein et al., 2008)Cited in: AI in Healthcare: A Brief History
• IBM Watson wins Jeopardy! (2011; Ferrucci et al., 2012)Cited in: AI in Healthcare: A Brief History
• identify disease outbreaks globallyCited in: AI in Healthcare: A Brief History
• INTERNIST-I/CADUCEUSCited in: AI in Healthcare: A Brief History
• Jiang et al., 2021Cited in: AI in Healthcare: A Brief History
• The Parable of Google Flu: Traps in Big Data AnalysisDavid Lazer; Ryan Kennedy; Gary King; et al. 2014. Sciencejournal articleCited in: AI in Healthcare: A Brief History (passage 1); AI in Healthcare: A Brief History (passage 2)Recommended in: AI in Healthcare: A Brief History (passage 3)
• A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysisXiaoxuan Liu; Livia Faes; Aditya U Kale; et al. 2019. The Lancet Digital Healthjournal articleCited in: AI in Healthcare: A Brief History (passage 1)Recommended in: AI in Healthcare: A Brief History (passage 2)
• McCarthy, 2007Cited in: AI in Healthcare: A Brief History
• Med-PaLM 2Cited in: AI in Healthcare: A Brief History
• Mikolov et al., 2013 on distributed representationsCited in: AI in Healthcare: A Brief History
• MYCINCited in: AI in Healthcare: A Brief History
• Nori et al., 2023Cited in: AI in Healthcare: A Brief History
• ONCOCINCited in: AI in Healthcare: A Brief History
• OpenAI, 2023Cited in: AI in Healthcare: A Brief History
• Researchers published the CheXNet preprintCited in: AI in Healthcare: A Brief History
• I.—COMPUTING MACHINERY AND INTELLIGENCEA. M. TURING. 1950. Mindjournal articleCited in: AI in Healthcare: A Brief History (passage 1)Recommended in: AI in Healthcare: A Brief History (passage 2)
• Vaswani et al., 2017: Attention Is All You NeedCited in: AI in Healthcare: A Brief History
• Antimicrobial Selection by a ComputerVictor L. Yu. 1979. JAMAjournal articleCited in: AI in Healthcare: A Brief History (passage 1); AI in Healthcare: A Brief History (passage 2)
• Zhou et al., 2018Cited in: AI in Healthcare: A Brief History

Recommended reading

• Machine Learning in MedicineAlvin Rajkomar; Jeffrey Dean; Isaac Kohane. 2019. New England Journal of Medicinejournal articleRecommended in: AI in Healthcare: A Brief History
• High-performance medicine: the convergence of human and artificial intelligenceEric J. Topol. 2019. Nature Medicinejournal articleRecommended in: AI in Healthcare: A Brief History

Machine Learning Fundamentals

References

• A unified approach to interpreting model predictions.Cited in: Machine Learning Fundamentals (passage 1)Recommended in: Machine Learning Fundamentals (passage 2)
• backpropagationCited in: Machine Learning Fundamentals
• BERTCited in: Machine Learning Fundamentals
• Bio_ClinicalBERTCited in: Machine Learning Fundamentals
• Random ForestsLeo Breiman. 2001. Machine Learningjournal articleCited in: Machine Learning Fundamentals (passage 1); Machine Learning Fundamentals (passage 2)Recommended in: Machine Learning Fundamentals (passage 3)
• Breiman et al.’s seminal CART workCited in: Machine Learning Fundamentals
• Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designsThomas Burnett; Pavel Mozgunov; Philip Pallmann; et al. 2020. BMC Medicinejournal articleCited in: Machine Learning Fundamentals
• CatBoostCited in: Machine Learning Fundamentals
• Chawla et al. (2002)Cited in: Machine Learning Fundamentals
• XGBoostTianqi Chen; Carlos Guestrin. 2016. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Miningconference paperCited in: Machine Learning Fundamentals
• Cross-validationCited in: Machine Learning Fundamentals
• DBSCANCited in: Machine Learning Fundamentals
• Deep Learning bookCited in: Machine Learning Fundamentals (passage 1); Machine Learning Fundamentals (passage 2); Machine Learning Fundamentals (passage 3)Recommended in: Machine Learning Fundamentals (passage 4)
• Domingos’ influential paper on machine learning pitfallsCited in: Machine Learning Fundamentals
• Dermatologist-level classification of skin cancer with deep neural networksAndre Esteva; Brett Kuprel; Roberto A. Novoa; et al. 2017. Naturejournal articleCited in: Machine Learning Fundamentals
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle PropertiesJianqing Fan; Runze Li. 2001. Journal of the American Statistical Associationjournal articleCited in: Machine Learning Fundamentals (passage 1); Machine Learning Fundamentals (passage 2)
• Comparison of pasting properties measured from the whole grain flour and extracted starch in barley (Hordeum vulgare L.)Xiangyun Fan; Juan Zhu; Wenbin Dong; et al. 2019. PLOS ONEjournal articleCited in: Machine Learning Fundamentals
• Greedy function approximation: A gradient boosting machine.Jerome H. Friedman. 2001. The Annals of Statisticsjournal articleCited in: Machine Learning Fundamentals (passage 1); Machine Learning Fundamentals (passage 2)Recommended in: Machine Learning Fundamentals (passage 3)
• Neural Networks and the Bias/Variance DilemmaStuart Geman; Elie Bienenstock; René Doursat. 1992. Neural Computationjournal articleCited in: Machine Learning Fundamentals
• Geron’s practical guideCited in: Machine Learning Fundamentals (passage 1); Machine Learning Fundamentals (passage 2)
• Learning to Forget: Continual Prediction with LSTMFelix A. Gers; Jürgen Schmidhuber; Fred Cummins. 2000. Neural Computationjournal articleCited in: Machine Learning Fundamentals
• Guidelines for reinforcement learning in healthcareOmer Gottesman; Fredrik Johansson; Matthieu Komorowski; et al. 2019. Nature Medicinejournal articleCited in: Machine Learning Fundamentals
• Grinsztajn et al., 2022, preprintCited in: Machine Learning Fundamentals (passage 1); Machine Learning Fundamentals (passage 2); Machine Learning Fundamentals (passage 3)
• Hastie et al.’s comprehensive statistical learning textCited in: Machine Learning Fundamentals
• A targeted real-time early warning score (TREWScore) for septic shockKatharine E. Henry; David N. Hager; Peter J. Pronovost; et al. 2015. Science Translational Medicinejournal articleCited in: Machine Learning Fundamentals
• Hernán and Robins, 2020Cited in: Machine Learning Fundamentals
• ImageNetCited in: Machine Learning Fundamentals
• introduced by van Rijsbergen (1979)Cited in: Machine Learning Fundamentals
• Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in ItalyYildiz Karadayi; Mehmet N. Aydin; Arif Selcuk Ogrenci. 2020. IEEE Accessjournal articleCited in: Machine Learning Fundamentals
• Kaufman et al.’s analysis of leakage in healthcare MLCited in: Machine Learning Fundamentals (passage 1); Machine Learning Fundamentals (passage 2)
• LightGBMCited in: Machine Learning Fundamentals
• Logistic regressionCited in: Machine Learning Fundamentals
• LSTM architecturesCited in: Machine Learning Fundamentals
• Mitchell’s foundational work on machine learningCited in: Machine Learning Fundamentals (passage 1); Machine Learning Fundamentals (passage 2)
• Inhomogeneity Based Characterization of Distribution Patterns on the Plasma MembraneLaura Paparelli; Nikky Corthout; Benjamin Pavie; et al. 2016. PLOS Computational Biologyjournal articleCited in: Machine Learning Fundamentals (passage 1); Machine Learning Fundamentals (passage 2)
• PubMedBERTCited in: Machine Learning Fundamentals
• Machine Learning in MedicineAlvin Rajkomar; Jeffrey Dean; Isaac Kohane. 2019. New England Journal of Medicinejournal articleCited in: Machine Learning Fundamentals (passage 1)Recommended in: Machine Learning Fundamentals (passage 2)
• Moving beyond AUC: decision curve analysis for quantifying net benefit of risk prediction modelsMohsen Sadatsafavi; Amin Adibi; Milo Puhan; et al. 2021. European Respiratory Journaljournal articleCited in: Machine Learning Fundamentals
• Estimating the Dimension of a ModelGideon Schwarz. 1978. The Annals of Statisticsjournal articleCited in: Machine Learning Fundamentals
• scikit-learnCited in: Machine Learning Fundamentals
• scikit-learn nested cross-validation guidanceCited in: Machine Learning Fundamentals
• Shwartz-Ziv & Armon, 2022, preprintCited in: Machine Learning Fundamentals (passage 1); Machine Learning Fundamentals (passage 2); Machine Learning Fundamentals (passage 3)
• Assessing the Performance of Prediction ModelsEwout W. Steyerberg; Andrew J. Vickers; Nancy R. Cook; et al. 2010. Epidemiologyjournal articleCited in: Machine Learning Fundamentals (passage 1); Machine Learning Fundamentals (passage 2)
• Sutton and Barto in their seminal textCited in: Machine Learning Fundamentals
• Is There a Sensory Threshold?John A. Swets. 1961. Sciencejournal articleCited in: Machine Learning Fundamentals
• t-SNECited in: Machine Learning Fundamentals
• COVID-19 Vaccine Distribution Policy Design with Reinforcement LearningPu Tan. 2021. 2021 5th International Conference on Advances in Image Processing (ICAIP)conference paperCited in: Machine Learning Fundamentals
• transfer learning approachCited in: Machine Learning Fundamentals
• UMAPCited in: Machine Learning Fundamentals
• universal approximatorsCited in: Machine Learning Fundamentals

Recommended reading

• An Introduction to Statistical LearningRecommended in: Machine Learning Fundamentals
• Andrew Ng’s Machine Learning SpecializationRecommended in: Machine Learning Fundamentals (passage 1); Machine Learning Fundamentals (passage 2)
• Artificial Intelligence in Public HealthRecommended in: Machine Learning Fundamentals
• Big Data and Machine Learning in Health CareRecommended in: Machine Learning Fundamentals
• Big Data and Machine Learning in Health Care.Recommended in: Machine Learning Fundamentals
• Statistics versus machine learningDanilo Bzdok; Naomi Altman; Martin Krzywinski. 2018. Nature Methodsjournal articleRecommended in: Machine Learning Fundamentals
• DeepLearning.AIRecommended in: Machine Learning Fundamentals
• Dive into Deep LearningRecommended in: Machine Learning Fundamentals
• Fast.aiRecommended in: Machine Learning Fundamentals
• Fast.ai: Practical Deep Learning for CodersRecommended in: Machine Learning Fundamentals
• Healthcare AI ResourcesRecommended in: Machine Learning Fundamentals
• Hugging FaceRecommended in: Machine Learning Fundamentals
• Kaggle Learn: Intro to MLRecommended in: Machine Learning Fundamentals
• Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleRecommended in: Machine Learning Fundamentals
• PyTorchRecommended in: Machine Learning Fundamentals
• "Why Should I Trust You?"Marco Tulio Ribeiro; Sameer Singh; Carlos Guestrin. 2016. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Miningconference paperRecommended in: Machine Learning Fundamentals
• Scikit-learn tutorialsRecommended in: Machine Learning Fundamentals
• Stanford CS229: Machine LearningRecommended in: Machine Learning Fundamentals
• TensorFlowRecommended in: Machine Learning Fundamentals
• this excellent guide on data leakageRecommended in: Machine Learning Fundamentals

The Data Problem in Public Health AI

References

• Box saidCited in: The Data Problem in Public Health AI
• CDC, April 2026Cited in: The Data Problem in Public Health AI
• changed multiple timesCited in: The Data Problem in Public Health AI
• Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United StatesEstee Y. Cramer; Evan L. Ray; Velma K. Lopez; et al. 2022. Proceedings of the National Academy of Sciencesjournal articleCited in: The Data Problem in Public Health AI
• Cramer et al., 2022Cited in: The Data Problem in Public Health AI (passage 1); The Data Problem in Public Health AI (passage 2)
• cytokine storm casesCited in: The Data Problem in Public Health AI
• Domingos’ influential paper on machine learning pitfallsCited in: The Data Problem in Public Health AI
• SARS-CoV-2 is associated with changes in brain structure in UK BiobankGwenaëlle Douaud; Soojin Lee; Fidel Alfaro-Almagro; et al. 2022. Naturejournal articleCited in: The Data Problem in Public Health AI (passage 1); The Data Problem in Public Health AI (passage 2)
• Hernán’s target trial frameworkCited in: The Data Problem in Public Health AI
• HIPAA Safe Harbor guidelinesCited in: The Data Problem in Public Health AI
• HL7 FHIRCited in: The Data Problem in Public Health AI
• IHME modelCited in: The Data Problem in Public Health AI
• Ioannidis cautionedCited in: The Data Problem in Public Health AI
• Leakage in data miningShachar Kaufman; Saharon Rosset; Claudia Perlich; et al. 2012. ACM Transactions on Knowledge Discovery from Datajournal articleCited in: The Data Problem in Public Health AI (passage 1)Recommended in: The Data Problem in Public Health AI (passage 2)
• Kim, Rogers & Wang, 2026Cited in: The Data Problem in Public Health AI
• Lee et al., 2021Cited in: The Data Problem in Public Health AI (passage 1); The Data Problem in Public Health AI (passage 2)
• Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)Ruiyun Li; Sen Pei; Bin Chen; et al. 2020. Sciencejournal articleCited in: The Data Problem in Public Health AI (passage 1)Recommended in: The Data Problem in Public Health AI (passage 2)
• Statistical Analysis with Missing DataRoderick J. A. Little; Donald B. Rubin. 2002. Wiley Series in Probability and StatisticsCited in: The Data Problem in Public Health AI (passage 1)Recommended in: The Data Problem in Public Health AI (passage 2)
• Effective containment explains subexponential growth in recent confirmed COVID-19 cases in ChinaBenjamin F. Maier; Dirk Brockmann. 2020. Sciencejournal articleCited in: The Data Problem in Public Health AI
• Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?Daniel J. McDonald; Jacob Bien; Alden Green; et al. 2021. Proceedings of the National Academy of Sciencesjournal articleCited in: The Data Problem in Public Health AI (passage 1); The Data Problem in Public Health AI (passage 2)Recommended in: The Data Problem in Public Health AI (passage 3)
• McGough et al.’s workCited in: The Data Problem in Public Health AI (passage 1); The Data Problem in Public Health AI (passage 2); The Data Problem in Public Health AI (passage 3)Recommended in: The Data Problem in Public Health AI (passage 4)
• A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United StatesNicholas G. Reich; Logan C. Brooks; Spencer J. Fox; et al. 2019. Proceedings of the National Academy of Sciencesjournal articleCited in: The Data Problem in Public Health AI (passage 1)Recommended in: The Data Problem in Public Health AI (passage 2)
• systematic underestimation of disease burdenCited in: The Data Problem in Public Health AI
• Weiskopf and Weng’s data quality frameworkCited in: The Data Problem in Public Health AI

Recommended reading

• CDC’s COVID-19 nowcasting approachRecommended in: The Data Problem in Public Health AI
• EpiNow2 R packageRecommended in: The Data Problem in Public Health AI
• Flexible Imputation of Missing DataRecommended in: The Data Problem in Public Health AI
• Changes in the quality of care during progress from stage 1 to stage 2 of Meaningful UseDavid M Levine; Michael J Healey; Adam Wright; et al. 2016. Journal of the American Medical Informatics Associationjournal articleRecommended in: The Data Problem in Public Health AI
• multiple imputationRecommended in: The Data Problem in Public Health AI
• Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical researchN. G. Weiskopf; C. Weng. 2013. Journal of the American Medical Informatics Associationjournal articleRecommended in: The Data Problem in Public Health AI

Part II: Current Applications

AI in Disease Surveillance and Outbreak Detection

References

• Artificial intelligence-enabled early warning systems for public health preparedness: perspectives of senior public health leaders in a Small Island Developing StateLetetia Addison; Shalini Pooransingh; Loren De Freitas. 2026. Frontiers in Public Healthjournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• AI strategyCited in: AI in Disease Surveillance and Outbreak Detection
• Alder, FedScoop, March 2026Cited in: AI in Disease Surveillance and Outbreak Detection
• ARGO: Combining search data with traditional surveillanceCited in: AI in Disease Surveillance and Outbreak Detection (passage 1)Recommended in: AI in Disease Surveillance and Outbreak Detection (passage 2)
• The Reliability of Tweets as a Supplementary Method of Seasonal Influenza SurveillanceAnoshé A Aslam; Ming-Hsiang Tsou; Brian H Spitzberg; et al. 2014. Journal of Medical Internet Researchjournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• BEACONCited in: AI in Disease Surveillance and Outbreak Detection
• A BEACON for Novel Disease Threats: Leveraging Artificial Intelligence for Informal Event-Based Outbreak SurveillanceNahid Bhadelia; Ioannis Ch Paschalidis; John S Brownstein; et al. 2025. The Journal of Infectious Diseasesjournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• AFP assistant: a retrieval-augmented generation and large language model-powered multilingual polio chatbot for low-resource language communitiesGelane Biru; Honey Gemechu; Firanol Teshome; et al. 2026. npj Digital Medicinejournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• BlueDotCited in: AI in Disease Surveillance and Outbreak Detection (passage 1); AI in Disease Surveillance and Outbreak Detection (passage 2)
• Potential for global spread of a novel coronavirus from ChinaIsaac I Bogoch; Alexander Watts; Andrea Thomas-Bachli; et al. 2020. Journal of Travel Medicinejournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• Buckeridge et al., 2007, JAMIACited in: AI in Disease Surveillance and Outbreak Detection (passage 1)Recommended in: AI in Disease Surveillance and Outbreak Detection (passage 2)
• Butler, 2013Cited in: AI in Disease Surveillance and Outbreak Detection
• CDC NWSSCited in: AI in Disease Surveillance and Outbreak Detection (passage 1); AI in Disease Surveillance and Outbreak Detection (passage 2)
• CDC’s Influenza Surveillance System (ILINet)Cited in: AI in Disease Surveillance and Outbreak Detection (passage 1)Recommended in: AI in Disease Surveillance and Outbreak Detection (passage 2)
• CDC’s NNDSSCited in: AI in Disease Surveillance and Outbreak Detection
• CDC’s NWSSCited in: AI in Disease Surveillance and Outbreak Detection (passage 1); AI in Disease Surveillance and Outbreak Detection (passage 2)
• CEPI partnered with UC Davis and BEACONCited in: AI in Disease Surveillance and Outbreak Detection
• Comparative studiesCited in: AI in Disease Surveillance and Outbreak Detection
• December 30, 2019 postCited in: AI in Disease Surveillance and Outbreak Detection
• Duke-NUS, December 2025Cited in: AI in Disease Surveillance and Outbreak Detection
• EIOS version 2.0Cited in: AI in Disease Surveillance and Outbreak Detection
• From fragmented to integrated surveillance in LMICs: digital pathways for outbreak detection and vaccine intelligenceDelfin Lovelina Francis; Saravanan Sampoornam Pape Reddy. 2026. Frontiers in Public Healthjournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• freely available on GitHub and Hugging FaceCited in: AI in Disease Surveillance and Outbreak Detection
• Freifeld et al., 2008, JAMIACited in: AI in Disease Surveillance and Outbreak Detection (passage 1)Recommended in: AI in Disease Surveillance and Outbreak Detection (passage 2)
• Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series AnalysisIris Ganser; Rodolphe Thiébaut; David L Buckeridge. 2022. JMIR Public Health and Surveillancejournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• A real-time early warning system to anticipate respiratory disease outbreaks using transfer learningRaul Garrido-Garcia; Leonardo Clemente; Austin G. Meyer; et al. 2026. Nature Communicationsjournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• A multi-agent system for automating scientific discoveryAli E. Ghareeb; Benjamin Chang; Ludovico Mitchener; et al. 2026. Naturejournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• Detecting influenza epidemics using search engine query dataJeremy Ginsberg; Matthew H. Mohebbi; Rajan S. Patel; et al. 2009. Naturejournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• Accelerating scientific discovery with Co-ScientistJuraj Gottweis; Wei-Hung Weng; Alexander Daryin; et al. 2026. Naturejournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• guidance for state, tribal, local, and territorial partnersCited in: AI in Disease Surveillance and Outbreak Detection
• Use of machine learning to detect Escherichia coli in drinking water in BangladeshIqramul Haq; Md. Yusuf Hossain Ador; Diego Nobrega. 2026. PLOS Onejournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• HealthMapCited in: AI in Disease Surveillance and Outbreak Detection (passage 1); AI in Disease Surveillance and Outbreak Detection (passage 2)
• HL7 FHIRCited in: AI in Disease Surveillance and Outbreak Detection
• HL7 messagingCited in: AI in Disease Surveillance and Outbreak Detection
• Houser et al., 2026, MMWRCited in: AI in Disease Surveillance and Outbreak Detection
• Unexplained Pauses in Centers for Disease Control and Prevention Surveillance: Erosion of the Public Evidence Base for Health PolicyJeremy W. Jacobs; Garrett S. Booth; Noel T. Brewer; et al. 2026. Annals of Internal Medicinejournal articleCited in: AI in Disease Surveillance and Outbreak Detection (passage 1); AI in Disease Surveillance and Outbreak Detection (passage 2)
• Jensen et al., MMWR, 2026Cited in: AI in Disease Surveillance and Outbreak Detection
• Global approaches to infectious disease surveillance and modelingMark P. Khurana; Joseph L.-H. Tsui; Bernardo Gutierrez; et al. 2026. Nature Medicinejournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• Kulldorff, 1997, Communications in StatisticsCited in: AI in Disease Surveillance and Outbreak Detection (passage 1)Recommended in: AI in Disease Surveillance and Outbreak Detection (passage 2)
• The Parable of Google Flu: Traps in Big Data AnalysisDavid Lazer; Ryan Kennedy; Gary King; et al. 2014. Sciencejournal articleCited in: AI in Disease Surveillance and Outbreak Detection (passage 1)Recommended in: AI in Disease Surveillance and Outbreak Detection (passage 2)
• Tampering With Evidence: Selective Silencing of Timely Public Health DataJeanne Marrazzo. 2026. Annals of Internal Medicinejournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• Mason et al., 2022Cited in: AI in Disease Surveillance and Outbreak Detection (passage 1); AI in Disease Surveillance and Outbreak Detection (passage 2)
• measles (wild-type)Cited in: AI in Disease Surveillance and Outbreak Detection
• Role of big data in the early detection of Ebola and other emerging infectious diseasesGabriel J Milinovich; Ricardo J Soares Magalhães; Wenbiao Hu. 2015. The Lancet Global Healthjournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• Cross-lingual transfer with multilingual language models for influenza-like-illness detection in social media textsNiti Mishra; Joan Ballester; Rodrigo Agerri. 2026. npj Digital Public Healthjournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• National Syndromic Surveillance ProgramCited in: AI in Disease Surveillance and Outbreak Detection
• PHINCited in: AI in Disease Surveillance and Outbreak Detection
• Early adverse physiological event detection using commercial wearables: challenges and opportunitiesJesse Phipps; Bryant Passage; Kaan Sel; et al. 2024. npj Digital Medicinejournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• ProMED-mailCited in: AI in Disease Surveillance and Outbreak Detection
• ProphetCited in: AI in Disease Surveillance and Outbreak Detection (passage 1); AI in Disease Surveillance and Outbreak Detection (passage 2)
• Explainable machine learning framework for foodborne disease outbreak prediction in Eastern Province, Saudi Arabia: case studyNaof Faiz Saleem Al-Ansary; Mahmoud Berekaa; Raghad Alhotheyfa; et al. 2026. Frontiers in Public Healthjournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• Salmon et al., 2015, Biometrical JournalCited in: AI in Disease Surveillance and Outbreak Detection
• state and local health departmentsCited in: AI in Disease Surveillance and Outbreak Detection
• system tracked spread in real-timeCited in: AI in Disease Surveillance and Outbreak Detection
• Artificial Intelligence and Complementary Digital Health Technologies Across the Travel Medicine Continuum: A Narrative ReviewPerry J J van Genderen; Eric N M van Sprang. 2026. Journal of Travel Medicinejournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• Ethical Challenges of Big Data in Public HealthEffy Vayena; Marcel Salathé; Lawrence C. Madoff; et al. 2015. PLOS Computational Biologyjournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• Artificial intelligence in early warning systems for infectious disease surveillance: a systematic reviewIsmael Villanueva-Miranda; Guanghua Xiao; Yang Xie. 2025. Frontiers in Public Healthjournal articleCited in: AI in Disease Surveillance and Outbreak Detection
• WastewaterSCANCited in: AI in Disease Surveillance and Outbreak Detection
• West Coast Health AllianceCited in: AI in Disease Surveillance and Outbreak Detection
• WHO, 2020Cited in: AI in Disease Surveillance and Outbreak Detection (passage 1); AI in Disease Surveillance and Outbreak Detection (passage 2); AI in Disease Surveillance and Outbreak Detection (passage 3); AI in Disease Surveillance and Outbreak Detection (passage 4)
• Xiang et al., 2026Cited in: AI in Disease Surveillance and Outbreak Detection
• Yang et al., 2026, AAAI Conference on Artificial IntelligenceCited in: AI in Disease Surveillance and Outbreak Detection

Recommended reading

• BioBERTRecommended in: AI in Disease Surveillance and Outbreak Detection
• Biobot AnalyticsRecommended in: AI in Disease Surveillance and Outbreak Detection
• CDC Surveillance Resource CenterRecommended in: AI in Disease Surveillance and Outbreak Detection
• download freeRecommended in: AI in Disease Surveillance and Outbreak Detection (passage 1); AI in Disease Surveillance and Outbreak Detection (passage 2)
• EIOSRecommended in: AI in Disease Surveillance and Outbreak Detection (passage 1); AI in Disease Surveillance and Outbreak Detection (passage 2)
• GluonTSRecommended in: AI in Disease Surveillance and Outbreak Detection
• Infectious Disease SurveillanceRecommended in: AI in Disease Surveillance and Outbreak Detection
• Modern Infectious Disease EpidemiologyRecommended in: AI in Disease Surveillance and Outbreak Detection
• R surveillance packageRecommended in: AI in Disease Surveillance and Outbreak Detection
• rsatscan packageRecommended in: AI in Disease Surveillance and Outbreak Detection
• scanstatistics R packageRecommended in: AI in Disease Surveillance and Outbreak Detection
• SciBERTRecommended in: AI in Disease Surveillance and Outbreak Detection
• See Google’s approachRecommended in: AI in Disease Surveillance and Outbreak Detection
• statsmodelsRecommended in: AI in Disease Surveillance and Outbreak Detection
• WastewaterSCANRecommended in: AI in Disease Surveillance and Outbreak Detection

Epidemic Forecasting with AI

References

• 2024–2025 FluSight evaluationCited in: Epidemic Forecasting with AI
• Beyond the 1.5°C planetary boundary: AI-forecast of Global Disease Burden and Health inequitiesHanif Abdul Rahman; Hein Minn Tun. 2026. PLOS Onejournal articleCited in: Epidemic Forecasting with AI
• ARIMACited in: Epidemic Forecasting with AI
• An AI system to help scientists write expert-level empirical softwareEser Aygün; Anastasiya Belyaeva; Gheorghe Comanici; et al. 2026. Naturejournal articleCited in: Epidemic Forecasting with AI
• butterfly effectCited in: Epidemic Forecasting with AI
• COVID-19 in the 47 countries of the WHO African region: a modelling analysis of past trends and future patternsJoseph Waogodo Cabore; Humphrey Cyprian Karamagi; Hillary Kipchumba Kipruto; et al. 2022. The Lancet Global Healthjournal articleCited in: Epidemic Forecasting with AI
• CDC’s FluSight challengeCited in: Epidemic Forecasting with AI (passage 1); Epidemic Forecasting with AI (passage 2)
• County, district and community-level measles transmission in the United States in 2013−2025Siyu Chen; Ana I. Bento. 2026. Nature Medicinejournal articleCited in: Epidemic Forecasting with AI
• Chimmula & Zhang, 2020, Chaos, Solitons & FractalsCited in: Epidemic Forecasting with AI (passage 1)Recommended in: Epidemic Forecasting with AI (passage 2)
• COVID-19 Forecast HubCited in: Epidemic Forecasting with AI (passage 1); Epidemic Forecasting with AI (passage 2); Epidemic Forecasting with AI (passage 3)
• Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United StatesEstee Y. Cramer; Evan L. Ray; Velma K. Lopez; et al. 2022. Proceedings of the National Academy of Sciencesjournal articleCited in: Epidemic Forecasting with AI (passage 1); Epidemic Forecasting with AI (passage 2)
• Cramer et al., 2022Cited in: Epidemic Forecasting with AI (passage 1); Epidemic Forecasting with AI (passage 2)
• Cramer et al., 2022, PNASCited in: Epidemic Forecasting with AI (passage 1)Recommended in: Epidemic Forecasting with AI (passage 2)
• Delamater et al., 2019, Emerging Infectious DiseasesCited in: Epidemic Forecasting with AI
• Advancing real-time infectious disease forecasting using large language modelsHongru Du; Yang Zhao; Jianan Zhao; et al. 2025. Nature Computational Sciencejournal articleCited in: Epidemic Forecasting with AI
• Forecast Hub weekly report, December 28, 2021Cited in: Epidemic Forecasting with AI (passage 1); Epidemic Forecasting with AI (passage 2)
• Tactical vs. strategic: An adaptable framework for horizon-dependent dengue forecasting using data-driven approaches with serotype and climate covariates in BangladeshMd. Muqtadir Fuad; Md. Sajib Milki; Ridwan Al Aziz. 2026. PLOS Onejournal articleCited in: Epidemic Forecasting with AI
• Enhancing epidemic forecasting with a physics-informed spatial identity neural networkSatoki Fujita; Tatsuya Akutsu. 2025. PLOS Onejournal articleCited in: Epidemic Forecasting with AI
• Gneiting & Raftery, 2007, Journal of the American Statistical AssociationCited in: Epidemic Forecasting with AI
• Great Storm of 1987Cited in: Epidemic Forecasting with AI
• Ioannidis et al., 2020, International Journal of ForecastingCited in: Epidemic Forecasting with AI (passage 1)Recommended in: Epidemic Forecasting with AI (passage 2)
• An open challenge to advance probabilistic forecasting for dengue epidemicsMichael A. Johansson; Karyn M. Apfeldorf; Scott Dobson; et al. 2019. Proceedings of the National Academy of Sciencesjournal articleCited in: Epidemic Forecasting with AI
• Keeling & Rohani, 2008, Modeling Infectious DiseasesCited in: Epidemic Forecasting with AI (passage 1)Recommended in: Epidemic Forecasting with AI (passage 2)
• LSTMCited in: Epidemic Forecasting with AI
• Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizationsSarabeth M. Mathis; Alexander E. Webber; Tomás M. León; et al. 2024. Nature Communicationsjournal articleCited in: Epidemic Forecasting with AI
• Michael Fish famously dismissedCited in: Epidemic Forecasting with AI
• A disease-agnostic approach to ensemble learning for infectious disease forecastingAlexander C. Murph; Lauren J. Beesley; G. Casey Gibson; et al. 2026. Nature Communicationsjournal articleCited in: Epidemic Forecasting with AI
• Omicron reviewCited in: Epidemic Forecasting with AI
• Prediction of infectious disease epidemics via weighted density ensemblesEvan L. Ray; Nicholas G. Reich. 2018. PLOS Computational Biologyjournal articleCited in: Epidemic Forecasting with AI
• Reich et al., 2019, PNASCited in: Epidemic Forecasting with AI (passage 1)Recommended in: Epidemic Forecasting with AI (passage 2)
• RespiCastCited in: Epidemic Forecasting with AI
• Predictive performance of multi-model ensemble forecasts of COVID-19 across European nationsKatharine Sherratt; Hugo Gruson; Rok Grah; et al. 2023. eLifejournal articleCited in: Epidemic Forecasting with AI
• Comparative evaluation of machine learning strategies for short-term dengue forecasting in Brazilian capital municipalitiesDarllan Collins da Cunha e Silva; Liliane Moreira Nery; Nícholas de Paula Nicomedes; et al. 2026. International Journal of Biometeorologyjournal articleCited in: Epidemic Forecasting with AI
• SIR modelCited in: Epidemic Forecasting with AI
• A multi-state climate epidemiology analysis of Lassa fever using interpretable machine learning and an early warning frameworkAyokunle John Tadema; Ayobami Emmanuel Mesioye; Sulymon A. Saka. 2026. BMC Infectious Diseasesjournal articleCited in: Epidemic Forecasting with AI
• Artificial intelligence in early warning systems for infectious disease surveillance: a systematic reviewIsmael Villanueva-Miranda; Guanghua Xiao; Yang Xie. 2025. Frontiers in Public Healthjournal articleCited in: Epidemic Forecasting with AI
• White House briefing, March 31, 2020Cited in: Epidemic Forecasting with AI

Recommended reading

• Coursera: EpidemicsRecommended in: Epidemic Forecasting with AI
• Coursera: Infectious Disease ModellingRecommended in: Epidemic Forecasting with AI
• COVID-19 Forecast Hub research publicationsRecommended in: Epidemic Forecasting with AI
• Delphi EpidataRecommended in: Epidemic Forecasting with AI
• EpiModel R packageRecommended in: Epidemic Forecasting with AI
• Hyndman & Athanasopoulos, Forecasting: Principles and PracticeRecommended in: Epidemic Forecasting with AI
• The RAPIDD ebola forecasting challenge: Synthesis and lessons learntCécile Viboud; Kaiyuan Sun; Robert Gaffey; et al. 2018. Epidemicsjournal articleRecommended in: Epidemic Forecasting with AI
• Vynnycky & White, 2010, An Introduction to Infectious Disease ModellingRecommended in: Epidemic Forecasting with AI

AI in Genomic Surveillance and Pathogen Analysis

References

• Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmissionDinesh Aggarwal; Ben Warne; Aminu S. Jahun; et al. 2022. Nature Communicationsjournal articleCited in: AI in Genomic Surveillance and Pathogen Analysis
• AMRnetCited in: AI in Genomic Surveillance and Pathogen Analysis
• Omicron extensively but incompletely escapes Pfizer BNT162b2 neutralizationSandile Cele; Laurelle Jackson; David S. Khoury; et al. 2022. Naturejournal articleCited in: AI in Genomic Surveillance and Pathogen Analysis
• AMRnet: a data visualization platform to interactively explore pathogen variants and antimicrobial resistanceLouise T Cerdeira. 2026. Nucleic Acids Researchjournal articleCited in: AI in Genomic Surveillance and Pathogen Analysis
• Profiling the gut resistome to unlock antimicrobial resistance biology and inform clinical riskZ. Chaudhry. 2026. Nature Communicationsjournal articleCited in: AI in Genomic Surveillance and Pathogen Analysis
• Cotton, U.S. Senate, 2026Cited in: AI in Genomic Surveillance and Pathogen Analysis
• cov-lineages.orgCited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1)Recommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 2); AI in Genomic Surveillance and Pathogen Analysis (passage 3)
• Delta variant (B.1.617.2)Cited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1); AI in Genomic Surveillance and Pathogen Analysis (passage 2)
• Felsenstein, 2004, Inferring PhylogeniesCited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1)Recommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 2)
• GISAIDCited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1)Recommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 2)
• Grubaugh et al., 2019, Nature MicrobiologyCited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1)Recommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 2)
• Hadfield et al., 2018, BioinformaticsCited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1)Recommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 2)
• Harvey et al., 2021, Nature Reviews MicrobiologyCited in: AI in Genomic Surveillance and Pathogen Analysis
• Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomesKaixin Hu. 2024. Briefings in Bioinformaticsjournal articleCited in: AI in Genomic Surveillance and Pathogen Analysis
• Inglesby et al., 2014, mBioCited in: AI in Genomic Surveillance and Pathogen Analysis
• Jansen et al., 2021, MMWRCited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1); AI in Genomic Surveillance and Pathogen Analysis (passage 2); AI in Genomic Surveillance and Pathogen Analysis (passage 3)
• Karthikeyan et al., 2022, NatureCited in: AI in Genomic Surveillance and Pathogen Analysis
• Wastewater surveillance reveals patterns of antibiotic resistance across the United StatesSooyeol Kim. 2026. Nature Communicationsjournal articleCited in: AI in Genomic Surveillance and Pathogen Analysis
• Convolutional neural networks quantify antibiotic resistance in Mycobacterium tuberculosis with diagnostic grade accuracy and predict treatment responseSanjana G. Kulkarni. 2026. Nature Communicationsjournal articleCited in: AI in Genomic Surveillance and Pathogen Analysis
• NextstrainCited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1)Recommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 2); AI in Genomic Surveillance and Pathogen Analysis (passage 3)
• Nextstrain clade namingCited in: AI in Genomic Surveillance and Pathogen Analysis
• PAHO documented sequencing as one component of the Cayman Islands’ One Health responseCited in: AI in Genomic Surveillance and Pathogen Analysis
• Rambaut et al., 2020, Nature MicrobiologyCited in: AI in Genomic Surveillance and Pathogen Analysis
• Sanjuán et al., 2010, Journal of VirologyCited in: AI in Genomic Surveillance and Pathogen Analysis
• Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA SequencingCRyPTIC Consortium and the 100,000 Genomes Project. 2018. New England Journal of Medicinejournal articleCited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1); AI in Genomic Surveillance and Pathogen Analysis (passage 2)Recommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 3)
• USGS, 2026Cited in: AI in Genomic Surveillance and Pathogen Analysis
• Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern AfricaRaquel Viana; Sikhulile Moyo; Daniel G. Amoako; et al. 2022. Naturejournal articleCited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1)Recommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 2)
• Predicting future hospital antimicrobial resistance prevalence using machine learningKarina-Doris Vihta. 2024. Communications Medicinejournal articleCited in: AI in Genomic Surveillance and Pathogen Analysis
• Virological, 2020Cited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1); AI in Genomic Surveillance and Pathogen Analysis (passage 2); AI in Genomic Surveillance and Pathogen Analysis (passage 3)
• Volz et al., 2021Cited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1)Recommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 2)
• Volz et al., 2021, NatureCited in: AI in Genomic Surveillance and Pathogen Analysis
• Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational studyTimothy M Walker; Camilla LC Ip; Ruth H Harrell; et al. 2013. The Lancet Infectious Diseasesjournal articleCited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1)Recommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 2)
• WHO Pandemic AgreementCited in: AI in Genomic Surveillance and Pathogen Analysis
• WHO variant trackingCited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1); AI in Genomic Surveillance and Pathogen Analysis (passage 2)
• Data sharing: Make outbreak research open accessNathan L. Yozwiak; Stephen F. Schaffner; Pardis C. Sabeti. 2015. Naturejournal articleCited in: AI in Genomic Surveillance and Pathogen Analysis (passage 1)Recommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 2)
• ~50% increase in transmissionCited in: AI in Genomic Surveillance and Pathogen Analysis

Recommended reading

• ARTIC Network protocolsRecommended in: AI in Genomic Surveillance and Pathogen Analysis
• BaseSpaceRecommended in: AI in Genomic Surveillance and Pathogen Analysis
• CDC NWSS dashboardRecommended in: AI in Genomic Surveillance and Pathogen Analysis
• COG-UK, 2020, Lancet MicrobeRecommended in: AI in Genomic Surveillance and Pathogen Analysis
• FreyjaRecommended in: AI in Genomic Surveillance and Pathogen Analysis
• GalaxyRecommended in: AI in Genomic Surveillance and Pathogen Analysis
• IQ-TREERecommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 1); AI in Genomic Surveillance and Pathogen Analysis (passage 2)
• NextcladeRecommended in: AI in Genomic Surveillance and Pathogen Analysis
• Pathogen Genomics for Public HealthRecommended in: AI in Genomic Surveillance and Pathogen Analysis
• Pevsner, 2015, Bioinformatics and Functional GenomicsRecommended in: AI in Genomic Surveillance and Pathogen Analysis
• snippyRecommended in: AI in Genomic Surveillance and Pathogen Analysis
• Terra.bioRecommended in: AI in Genomic Surveillance and Pathogen Analysis (passage 1); AI in Genomic Surveillance and Pathogen Analysis (passage 2)

Geospatial AI and Spatial Epidemiology

References

• CDC, 2026Cited in: Geospatial AI and Spatial Epidemiology (passage 1); Geospatial AI and Spatial Epidemiology (passage 2)
• CDC, 2026Cited in: Geospatial AI and Spatial Epidemiology
• CDC/ATSDR SVICited in: Geospatial AI and Spatial Epidemiology
• Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative ReviewHari S. Iyer; Seigi Karasaki; Li Yi; et al. 2025. Current Environmental Health Reportsjournal articleCited in: Geospatial AI and Spatial Epidemiology
• WHO GIS Centre for HealthCited in: Geospatial AI and Spatial Epidemiology
• WHO Regional Office for Europe, 2026Cited in: Geospatial AI and Spatial Epidemiology

AI in Diagnostic and Clinical Decision Support

References

• $1,000 per quality-adjusted life-yearCited in: AI in Diagnostic and Clinical Decision Support
• 10 million new TB cases annuallyCited in: AI in Diagnostic and Clinical Decision Support
• 12 million Americans affected annuallyCited in: AI in Diagnostic and Clinical Decision Support
• 2 hours on EHR for every 1 hour with patientsCited in: AI in Diagnostic and Clinical Decision Support
• 3.85 million 30-day all-cause readmissions in the US in 2020, averaging $16,300 each, 12.4% more than the index admissionCited in: AI in Diagnostic and Clinical Decision Support
• 30% false negative rateCited in: AI in Diagnostic and Clinical Decision Support
• 463 million people with diabetes globallyCited in: AI in Diagnostic and Clinical Decision Support
• AbridgeCited in: AI in Diagnostic and Clinical Decision Support
• Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: AI in Diagnostic and Clinical Decision Support (passage 1); AI in Diagnostic and Clinical Decision Support (passage 2); AI in Diagnostic and Clinical Decision Support (passage 3)
• AidocCited in: AI in Diagnostic and Clinical Decision Support
• Ancker et al., 2017, BMJ Quality & SafetyCited in: AI in Diagnostic and Clinical Decision Support
• Apple WatchCited in: AI in Diagnostic and Clinical Decision Support
• Ardila et al., 2019, Nature MedicineCited in: AI in Diagnostic and Clinical Decision Support
• A clinical decision support tool for accurate hip fracture prediction: A nationwide cohort studyKristian F. Axelsson; Henrik Litsne; Konstantinos Konstantinou; et al. 2026. PLOS Medicinejournal articleCited in: AI in Diagnostic and Clinical Decision Support
• AI-supported early autism identification as public health infrastructure: economic implications for the United States using a scenario-based economic modelTannista Banerjee; Arnab Nayak. 2026. Frontiers in Public Healthjournal articleCited in: AI in Diagnostic and Clinical Decision Support
• Beam & Kohane, 2018, JAMACited in: AI in Diagnostic and Clinical Decision Support
• BioGPTCited in: AI in Diagnostic and Clinical Decision Support
• Bright et al., 2012, JAMACited in: AI in Diagnostic and Clinical Decision Support
• Chouldechova, 2017, Big Data (arXiv preprint)Cited in: AI in Diagnostic and Clinical Decision Support
• Churpek et al., 2016, JAMA Internal MedicineCited in: AI in Diagnostic and Clinical Decision Support
• Conway et al., 2013, JMIRCited in: AI in Diagnostic and Clinical Decision Support
• Current legal frameworkCited in: AI in Diagnostic and Clinical Decision Support
• Dembrower et al., 2020, RadiologyCited in: AI in Diagnostic and Clinical Decision Support
• EkoCited in: AI in Diagnostic and Clinical Decision Support
• Epic Deterioration IndexCited in: AI in Diagnostic and Clinical Decision Support
• Dermatologist-level classification of skin cancer with deep neural networksAndre Esteva; Brett Kuprel; Roberto A. Novoa; et al. 2017. Naturejournal articleCited in: AI in Diagnostic and Clinical Decision Support (passage 1); AI in Diagnostic and Clinical Decision Support (passage 2)Recommended in: AI in Diagnostic and Clinical Decision Support (passage 3)
• false positive rate 96.4%Cited in: AI in Diagnostic and Clinical Decision Support
• FDA DEN180001Cited in: AI in Diagnostic and Clinical Decision Support (passage 1); AI in Diagnostic and Clinical Decision Support (passage 2); AI in Diagnostic and Clinical Decision Support (passage 3)
• FDA digital-health guidance indexCited in: AI in Diagnostic and Clinical Decision Support
• FDA, 2021 - AI/ML Action PlanCited in: AI in Diagnostic and Clinical Decision Support (passage 1)Recommended in: AI in Diagnostic and Clinical Decision Support (passage 2)
• Finlayson et al., 2019, ScienceCited in: AI in Diagnostic and Clinical Decision Support
• Grad-CAMCited in: AI in Diagnostic and Clinical Decision Support
• Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus PhotographsVarun Gulshan; Lily Peng; Marc Coram; et al. 2016. JAMAjournal articleCited in: AI in Diagnostic and Clinical Decision Support
• Gulshan et al., 2016, JAMACited in: AI in Diagnostic and Clinical Decision Support (passage 1)Recommended in: AI in Diagnostic and Clinical Decision Support (passage 2)
• International Medical Device Regulators Forum (IMDRF)Cited in: AI in Diagnostic and Clinical Decision Support
• Predicting hyperopic reserve in children based on school-based vision programs with ensemble learningJing Jiang; Yu Lu; Meng Li; et al. 2026. Frontiers in Public Healthjournal articleCited in: AI in Diagnostic and Clinical Decision Support
• Krittanawong et al., 2020, European Heart JournalCited in: AI in Diagnostic and Clinical Decision Support
• A community-codesigned LLM-powered chatbot for primary care: a randomized controlled trialSairan Li; Yanzeng Li; Shuya Zhou; et al. 2026. Nature Healthjournal articleCited in: AI in Diagnostic and Clinical Decision Support
• A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysisXiaoxuan Liu; Livia Faes; Aditya U Kale; et al. 2019. The Lancet Digital Healthjournal articleCited in: AI in Diagnostic and Clinical Decision Support (passage 1); AI in Diagnostic and Clinical Decision Support (passage 2)
• Ambient artificial intelligence scribes: utilization and impact on documentation timeStephen P Ma; April S Liang; Shreya J Shah; et al. 2025. Journal of the American Medical Informatics Associationjournal articleCited in: AI in Diagnostic and Clinical Decision Support
• McKinney et al., 2020, NatureCited in: AI in Diagnostic and Clinical Decision Support (passage 1)Recommended in: AI in Diagnostic and Clinical Decision Support (passage 2)
• Med-PaLM 2Cited in: AI in Diagnostic and Clinical Decision Support
• Med-PaLM Multimodal (Med-PaLM M)Cited in: AI in Diagnostic and Clinical Decision Support
• Medical Device Regulation (MDR)Cited in: AI in Diagnostic and Clinical Decision Support
• Microsoft Dragon CopilotCited in: AI in Diagnostic and Clinical Decision Support
• MYCINCited in: AI in Diagnostic and Clinical Decision Support
• Nabla CopilotCited in: AI in Diagnostic and Clinical Decision Support
• Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: AI in Diagnostic and Clinical Decision Support (passage 1)Recommended in: AI in Diagnostic and Clinical Decision Support (passage 2)
• Large-Scale Assessment of a Smartwatch to Identify Atrial FibrillationMarco V. Perez; Kenneth W. Mahaffey; Haley Hedlin; et al. 2019. New England Journal of Medicinejournal articleCited in: AI in Diagnostic and Clinical Decision Support
• Physicians override 49-96% of medication alertsCited in: AI in Diagnostic and Clinical Decision Support
• Price, 2018, in Big Data, Health Law, and Bioethics (Cambridge)Cited in: AI in Diagnostic and Clinical Decision Support
• Qin et al., 2021, Lancet Digital HealthCited in: AI in Diagnostic and Clinical Decision Support
• Quiroz et al., 2020Cited in: AI in Diagnostic and Clinical Decision Support
• Researchers published the CheXNet preprintCited in: AI in Diagnostic and Clinical Decision Support (passage 1); AI in Diagnostic and Clinical Decision Support (passage 2); AI in Diagnostic and Clinical Decision Support (passage 3)
• Researchers validate Epic’s sepsis prediction modelCited in: AI in Diagnostic and Clinical Decision Support (passage 1); AI in Diagnostic and Clinical Decision Support (passage 2)
• Ruamviboonsuk et al., 2019, OphthalmologyCited in: AI in Diagnostic and Clinical Decision Support
• Sendak et al., 2020, JMIR Medical InformaticsCited in: AI in Diagnostic and Clinical Decision Support (passage 1)Recommended in: AI in Diagnostic and Clinical Decision Support (passage 2)
• Sjoding et al., 2020, NEJMCited in: AI in Diagnostic and Clinical Decision Support
• Software and AI as Medical Device Change ProgrammeCited in: AI in Diagnostic and Clinical Decision Support
• Suki.AICited in: AI in Diagnostic and Clinical Decision Support
• Sutton et al., 2020, JAMACited in: AI in Diagnostic and Clinical Decision Support
• An LLM chatbot to facilitate primary-to-specialist care transitions: a randomized controlled trialXinge Tao; Shuya Zhou; Kai Ding; et al. 2026. Nature Medicinejournal articleCited in: AI in Diagnostic and Clinical Decision Support
• A clinically applicable approach to continuous prediction of future acute kidney injuryNenad Tomašev; Xavier Glorot; Jack W. Rae; et al. 2019. Naturejournal articleCited in: AI in Diagnostic and Clinical Decision Support
• Tonekaboni et al., 2019, ML4H (arXiv preprint)Cited in: AI in Diagnostic and Clinical Decision Support
• Automated Diabetic Retinopathy Image Assessment SoftwareAdnan Tufail; Caroline Rudisill; Catherine Egan; et al. 2017. Ophthalmologyjournal articleCited in: AI in Diagnostic and Clinical Decision Support
• Implementing a chest X-ray artificial intelligence tool to enhance tuberculosis screening in India: Lessons learnedShibu Vijayan; Vaishnavi Jondhale; Tripti Pande; et al. 2023. PLOS Digital Healthjournal articleCited in: AI in Diagnostic and Clinical Decision Support
• General-purpose large language models outperform specialized clinical AI tools on medical benchmarksKrithik Vishwanath; Anton Alyakin; Mrigayu Ghosh; et al. 2026. Nature Medicinejournal articleCited in: AI in Diagnostic and Clinical Decision Support
• Wang et al., 2009, JAMIACited in: AI in Diagnostic and Clinical Decision Support
• Evaluating skin tone scales for dermatologic dataset labeling: a prospective-comparative studyVanessa R. Weir; Yingjoy Li; Maura C. Gillis; et al. 2025. npj Digital Medicinejournal articleCited in: AI in Diagnostic and Clinical Decision Support
• Wong et al., 2021Cited in: AI in Diagnostic and Clinical Decision Support (passage 1); AI in Diagnostic and Clinical Decision Support (passage 2)
• Wong et al., 2021Cited in: AI in Diagnostic and Clinical Decision Support
• Yoon et al., 2019, JAMA Network OpenCited in: AI in Diagnostic and Clinical Decision Support
• Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional studyJohn R. Zech; Marcus A. Badgeley; Manway Liu; et al. 2018. PLOS Medicinejournal articleCited in: AI in Diagnostic and Clinical Decision Support (passage 1); AI in Diagnostic and Clinical Decision Support (passage 2)

Recommended reading

• Adamson & Smith, 2018, NEJM - ML and disparitiesRecommended in: AI in Diagnostic and Clinical Decision Support
• AI Fairness 360Recommended in: AI in Diagnostic and Clinical Decision Support
• Char et al., 2018, NEJM - Implementing ML in Health Care: Ethical ChallengesRecommended in: AI in Diagnostic and Clinical Decision Support
• CheXpertRecommended in: AI in Diagnostic and Clinical Decision Support
• Coursera: AI for Medicine SpecializationRecommended in: AI in Diagnostic and Clinical Decision Support
• Disparities in dermatology AI performance on a diverse, curated clinical image setRoxana Daneshjou; Kailas Vodrahalli; Roberto A. Novoa; et al. 2022. Science Advancesjournal articleRecommended in: AI in Diagnostic and Clinical Decision Support
• ECG screening evidence in the Physician AI HandbookRecommended in: AI in Diagnostic and Clinical Decision Support
• eICURecommended in: AI in Diagnostic and Clinical Decision Support
• FairlearnRecommended in: AI in Diagnostic and Clinical Decision Support (passage 1); AI in Diagnostic and Clinical Decision Support (passage 2)
• Fast.ai: Practical Deep Learning for CodersRecommended in: AI in Diagnostic and Clinical Decision Support
• FDA AI-Enabled Medical Device ListRecommended in: AI in Diagnostic and Clinical Decision Support
• Large Language Models in Clinical PracticeRecommended in: AI in Diagnostic and Clinical Decision Support
• Liu et al., 2020, Lancet Digital Health - Systematic review of imaging AIRecommended in: AI in Diagnostic and Clinical Decision Support
• MIMIC-CXRRecommended in: AI in Diagnostic and Clinical Decision Support
• MIMIC-IIIRecommended in: AI in Diagnostic and Clinical Decision Support
• NIH Chest X-ray DatasetRecommended in: AI in Diagnostic and Clinical Decision Support
• Oakden-Rayner et al., 2020, Proc ACM CHIL - Hidden stratificationRecommended in: AI in Diagnostic and Clinical Decision Support
• Physician AI Handbook’s dermatology chapterRecommended in: AI in Diagnostic and Clinical Decision Support (passage 1); AI in Diagnostic and Clinical Decision Support (passage 2)
• Shah et al., 2019, JAMA - Making ML work clinicallyRecommended in: AI in Diagnostic and Clinical Decision Support
• SHAPRecommended in: AI in Diagnostic and Clinical Decision Support
• Stanford CS 271: AI in HealthcareRecommended in: AI in Diagnostic and Clinical Decision Support
• The Physician AI HandbookRecommended in: AI in Diagnostic and Clinical Decision Support
• Topol, E., 2019, Deep MedicineRecommended in: AI in Diagnostic and Clinical Decision Support
• WHO Ethics and Governance of AI for HealthRecommended in: AI in Diagnostic and Clinical Decision Support

AI for Substance Use and Overdose Prevention

References

• Afshar et al., 2025Cited in: AI for Substance Use and Overdose Prevention (passage 1); AI for Substance Use and Overdose Prevention (passage 2)Recommended in: AI for Substance Use and Overdose Prevention (passage 3)
• CDC clinical guidanceCited in: AI for Substance Use and Overdose Prevention (passage 1); AI for Substance Use and Overdose Prevention (passage 2)
• CDC, 2024Cited in: AI for Substance Use and Overdose Prevention
• CDC, 2026Cited in: AI for Substance Use and Overdose Prevention (passage 1); AI for Substance Use and Overdose Prevention (passage 2); AI for Substance Use and Overdose Prevention (passage 3)
• FDA, 2017Cited in: AI for Substance Use and Overdose Prevention
• HEAL Opioid Use Disorder and Overdose Strategic PlanCited in: AI for Substance Use and Overdose Prevention (passage 1); AI for Substance Use and Overdose Prevention (passage 2)
• HHS, 2024Cited in: AI for Substance Use and Overdose Prevention (passage 1); AI for Substance Use and Overdose Prevention (passage 2); AI for Substance Use and Overdose Prevention (passage 3)
• HHS, Feb 2024Cited in: AI for Substance Use and Overdose Prevention
• Lo-Ciganic et al., 2022, Lancet Digital HealthCited in: AI for Substance Use and Overdose Prevention (passage 1)Recommended in: AI for Substance Use and Overdose Prevention (passage 2)
• Algorithmic opacity in opioid risk scoring and the need for transparent AI regulationSherry Yun Wang; Ryan Stofer; Zhouzhou Chu; et al. 2026. npj Digital Medicinejournal articleCited in: AI for Substance Use and Overdose Prevention

Recommended reading

• ASAMRecommended in: AI for Substance Use and Overdose Prevention
• Legal Action CenterRecommended in: AI for Substance Use and Overdose Prevention
• SAMHSARecommended in: AI for Substance Use and Overdose Prevention

Part III: Implementation and Evaluation

Evaluating AI Systems for Healthcare

References

• 1,357 AI medical devices cleared, 3 actually tested on patient outcomesRawan Abulibdeh; Sebastián Andrés Cajas Ordóñez; Leo Anthony Celi; et al. 2026. PLOS Digital Healthjournal articleCited in: Evaluating AI Systems for Healthcare
• Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trialAmbrose Agweyu; Paul Mwaniki; Vaishnavi Menon; et al. 2026. Nature Medicinejournal articleCited in: Evaluating AI Systems for Healthcare
• Ancker et al., 2017, BMC Medical Informatics and Decision MakingCited in: Evaluating AI Systems for Healthcare
• Principles to guide clinical AI readiness and move from benchmarks to real-world evaluationTej D. Azad; Harlan M. Krumholz; Suchi Saria. 2026. Nature Medicinejournal articleCited in: Evaluating AI Systems for Healthcare
• Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a RealityDavid W. Bates; Gilad J. Kuperman; Samuel Wang; et al. 2003. Journal of the American Medical Informatics Associationjournal articleCited in: Evaluating AI Systems for Healthcare
• CDC ACIP GRADE Handbook, 2024Cited in: Evaluating AI Systems for Healthcare
• CDC, 2001Cited in: Evaluating AI Systems for Healthcare
• CHART statementCited in: Evaluating AI Systems for Healthcare
• TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methodsGary S Collins; Karel G M Moons; Paula Dhiman; et al. 2024. BMJjournal articleCited in: Evaluating AI Systems for Healthcare (passage 1); Evaluating AI Systems for Healthcare (passage 3)Recommended in: Evaluating AI Systems for Healthcare (passage 2)
• Collins et al., 2015, BMJCited in: Evaluating AI Systems for Healthcare (passage 1)Recommended in: Evaluating AI Systems for Healthcare (passage 2)
• Davis et al., 2017, JAMIACited in: Evaluating AI Systems for Healthcare
• EU AI Act, 2024Cited in: Evaluating AI Systems for Healthcare (passage 1)Recommended in: Evaluating AI Systems for Healthcare (passage 2)
• Gabriel and Olawuyi, 2026, preprintCited in: Evaluating AI Systems for Healthcare
• Gabriel et al., 2026, preprintCited in: Evaluating AI Systems for Healthcare
• The TRIPOD-LLM reporting guideline for studies using large language modelsJack Gallifant; Majid Afshar; Saleem Ameen; et al. 2025. Nature Medicinejournal articleCited in: Evaluating AI Systems for Healthcare
• Geirhos et al., 2020, Nature Machine IntelligenceCited in: Evaluating AI Systems for Healthcare
• Gianfrancesco et al., 2018, JAMA Internal MedicineCited in: Evaluating AI Systems for Healthcare
• Good Machine Learning Practice for Medical Device DevelopmentCited in: Evaluating AI Systems for Healthcare
• Large Language Models for Chatbot Health Advice StudiesBright Huo; Amy Boyle; Nana Marfo; et al. 2025. JAMA Network Openjournal articleCited in: Evaluating AI Systems for Healthcare
• Artificial Intelligence and Machine Learning-based prediction of tuberculosis treatment failure: A systematic review and meta-analysisRogers Kamulegeya; Rose Nabatanzi; Derrick Semugenze; et al. 2026. PLOS Onejournal articleCited in: Evaluating AI Systems for Healthcare
• Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published PapersDong Wook Kim; Hye Young Jang; Kyung Won Kim; et al. 2019. Korean Journal of Radiologyjournal articleCited in: Evaluating AI Systems for Healthcare
• Kim et al.Cited in: Evaluating AI Systems for Healthcare
• Prospective evaluation of a large language model clinical decision support system in the emergency departmentLiron Leibovitch; Adi Ahituv; Alon Gorenshtein; et al. 2026. Nature Medicinejournal articleCited in: Evaluating AI Systems for Healthcare
• Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extensionXiaoxuan Liu; Samantha Cruz Rivera; David Moher; et al. 2020. Nature Medicinejournal articleCited in: Evaluating AI Systems for Healthcare (passage 1)Recommended in: Evaluating AI Systems for Healthcare (passage 2)
• McKinsey’s 2025 cross-industry State of AI surveyCited in: Evaluating AI Systems for Healthcare
• PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methodsKarel G M Moons; Johanna A A Damen; Tabea Kaul; et al. 2025. BMJjournal articleCited in: Evaluating AI Systems for Healthcare (passage 1)Recommended in: Evaluating AI Systems for Healthcare (passage 2)
• NICE Evidence Standards FrameworkCited in: Evaluating AI Systems for Healthcare
• Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: Evaluating AI Systems for Healthcare (passage 1)Recommended in: Evaluating AI Systems for Healthcare (passage 2)
• Addressing benchmarking gaps in large language models for health and medicine with dynamic red-teamingJiazhen Pan; Bailiang Jian; Paul Hager; et al. 2026. Nature Healthjournal articleCited in: Evaluating AI Systems for Healthcare
• The epidemiology of artificial intelligenceHarsh Parikh; Tyler McCormick; Emily K. Johnson; et al. 2026. Nature Healthjournal articleCited in: Evaluating AI Systems for Healthcare (passage 1)Recommended in: Evaluating AI Systems for Healthcare (passage 2)
• Philipp et al., 2026, preprintCited in: Evaluating AI Systems for Healthcare
• Predetermined Change Control Plans for ML-Enabled DevicesCited in: Evaluating AI Systems for Healthcare
• Proctor et al., 2011, Administration and Policy in Mental HealthCited in: Evaluating AI Systems for Healthcare (passage 1); Evaluating AI Systems for Healthcare (passage 2)Recommended in: Evaluating AI Systems for Healthcare (passage 3)
• Real-world analysis of depression-related adverse events associated with the adsorbed anthrax vaccine: integrating pharmacovigilance signals, machine learning risk prediction, and transcriptomic mechanismsGuangwei Qing; Yimei Zhao; Jian Yang; et al. 2026. Infectious Diseases of Povertyjournal articleCited in: Evaluating AI Systems for Healthcare
• Physicians and artificial intelligence diverge in evaluating large language models on real clinical casesPeilun Shi; Jian Li; Ziqi Yang; et al. 2026. npj Digital Medicinejournal articleCited in: Evaluating AI Systems for Healthcare
• STARD guidelinesCited in: Evaluating AI Systems for Healthcare
• Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AIBaptiste Vasey; Myura Nagendran; Bruce Campbell; et al. 2022. Nature Medicinejournal articleCited in: Evaluating AI Systems for Healthcare (passage 1)Recommended in: Evaluating AI Systems for Healthcare (passage 2)
• Vickers et al., 2019, Diagnostic and Prognostic ResearchCited in: Evaluating AI Systems for Healthcare
• Wong et al., 2021Cited in: Evaluating AI Systems for Healthcare (passage 1); Evaluating AI Systems for Healthcare (passage 2); Evaluating AI Systems for Healthcare (passage 3); Evaluating AI Systems for Healthcare (passage 4)
• A five-phase evaluation framework for diagnostic and predictive medical artificial intelligenceZichen Ye; Yue Chen; Xuefeng Huang; et al. 2026. npj Digital Medicinejournal articleCited in: Evaluating AI Systems for Healthcare
• Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional studyJohn R. Zech; Marcus A. Badgeley; Manway Liu; et al. 2018. PLOS Medicinejournal articleCited in: Evaluating AI Systems for Healthcare

Recommended reading

• AequitasRecommended in: Evaluating AI Systems for Healthcare
• AI Fairness 360Recommended in: Evaluating AI Systems for Healthcare
• Barocas et al., 2023, Fairness and Machine LearningRecommended in: Evaluating AI Systems for Healthcare
• Chouldechova, 2017, FAT - Impossibility theoremRecommended in: Evaluating AI Systems for Healthcare
• Digital Health Center of ExcellenceRecommended in: Evaluating AI Systems for Healthcare
• CONSORT-EHEALTH: Improving and Standardizing Evaluation Reports of Web-based and Mobile Health InterventionsGunther Eysenbach; CONSORT-EHEALTH Group. 2011. Journal of Medical Internet Researchjournal articleRecommended in: Evaluating AI Systems for Healthcare
• FairlearnRecommended in: Evaluating AI Systems for Healthcare
• FDA, 2021 - AI/ML Action PlanRecommended in: Evaluating AI Systems for Healthcare
• IMDRF SaMD FrameworkRecommended in: Evaluating AI Systems for Healthcare
• LIMERecommended in: Evaluating AI Systems for Healthcare
• Liu et al., 2019, Radiology - Medical imaging AI systematic reviewRecommended in: Evaluating AI Systems for Healthcare
• Oakden-Rayner et al., 2020, Proc ACM CHIL - Hidden stratificationRecommended in: Evaluating AI Systems for Healthcare
• Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI ExtensionSamantha Cruz Rivera; Xiaoxuan Liu; An-Wen Chan; et al. 2020. BMJjournal articleRecommended in: Evaluating AI Systems for Healthcare
• Scikit-learnRecommended in: Evaluating AI Systems for Healthcare
• Scikit-survivalRecommended in: Evaluating AI Systems for Healthcare
• SHAPRecommended in: Evaluating AI Systems for Healthcare
• Software as a Medical Device GuidanceRecommended in: Evaluating AI Systems for Healthcare
• Steyerberg, 2019, Clinical Prediction ModelsRecommended in: Evaluating AI Systems for Healthcare
• Vickers & Elkin, 2006, Medical Decision Making - Decision curve analysisRecommended in: Evaluating AI Systems for Healthcare

Performance Metrics for Public Health AI

References

• Altman & Bland, 1994, BMJCited in: Performance Metrics for Public Health AI
• ARISE, 2026Cited in: Performance Metrics for Public Health AI
• Reliability of LLMs as medical assistants for the general public: a randomized preregistered studyAndrew M. Bean; Rebecca Elizabeth Payne; Guy Parsons; et al. 2026. Nature Medicinejournal articleCited in: Performance Metrics for Public Health AI
• Guo et al., 2017, ICMLCited in: Performance Metrics for Public Health AI
• Hanley & McNeil, 1982, RadiologyCited in: Performance Metrics for Public Health AI
• Harrell et al., 1982, JAMACited in: Performance Metrics for Public Health AI
• PubMedQA: A Dataset for Biomedical Research Question AnsweringQiao Jin; Bhuwan Dhingra; Zhengping Liu; et al. 2019. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)conference paperCited in: Performance Metrics for Public Health AI
• What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical ExamsDi Jin; Eileen Pan; Nassim Oufattole; et al. 2021. Applied Sciencesjournal articleCited in: Performance Metrics for Public Health AI
• Miller, 2024, preprintCited in: Performance Metrics for Public Health AI
• Pal et al., 2022Cited in: Performance Metrics for Public Health AI
• Rufibach, 2010, Clinical TrialsCited in: Performance Metrics for Public Health AI
• Saito & Rehmsmeier, 2015, PLOS ONECited in: Performance Metrics for Public Health AI
• Large language models encode clinical knowledgeKaran Singhal; Shekoofeh Azizi; Tao Tu; et al. 2023. Naturejournal articleCited in: Performance Metrics for Public Health AI (passage 1); Performance Metrics for Public Health AI (passage 2)
• General-purpose large language models outperform specialized clinical AI tools on medical benchmarksKrithik Vishwanath; Anton Alyakin; Mrigayu Ghosh; et al. 2026. Nature Medicinejournal articleCited in: Performance Metrics for Public Health AI

Recommended reading

• The Physician AI HandbookRecommended in: Performance Metrics for Public Health AI

Validation, Equity, and Security Testing

References

• Buolamwini & Gebru, 2018, FATCited in: Validation, Equity, and Security Testing
• Chouldechova, 2017, FAT - Impossibility theoremCited in: Validation, Equity, and Security Testing
• Davis et al., 2017, JAMIACited in: Validation, Equity, and Security Testing
• DeGrave et al., 2021, Nature Machine IntelligenceCited in: Validation, Equity, and Security Testing
• EU AI Act, 2024Cited in: Validation, Equity, and Security Testing
• Finlayson et al., 2019, ScienceCited in: Validation, Equity, and Security Testing
• Gichoya et al., 2022, Lancet Digital HealthCited in: Validation, Equity, and Security Testing
• Kaufman et al., 2012, SIGKDDCited in: Validation, Equity, and Security Testing
• Kleinberg et al., 2017, ITCSCited in: Validation, Equity, and Security Testing
• McKinney et al., 2020, NatureCited in: Validation, Equity, and Security Testing
• Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: Validation, Equity, and Security Testing
• Pencina et al., 2008, Statistics in MedicineCited in: Validation, Equity, and Security Testing
• Researchers published the CheXNet preprintCited in: Validation, Equity, and Security Testing
• Semler et al., 2018, NEJMCited in: Validation, Equity, and Security Testing
• Sjoding et al., 2020, NEJMCited in: Validation, Equity, and Security Testing
• A clinically applicable approach to continuous prediction of future acute kidney injuryNenad Tomašev; Xavier Glorot; Jack W. Rae; et al. 2019. Naturejournal articleCited in: Validation, Equity, and Security Testing
• Vickers & Elkin, 2006, Medical Decision Making - Decision curve analysisCited in: Validation, Equity, and Security Testing
• Vickers et al., 2019, Diagnostic and Prognostic ResearchCited in: Validation, Equity, and Security Testing
• Zech et al., 2018, PLOS MedicineCited in: Validation, Equity, and Security Testing

Recommended reading

• FairlearnRecommended in: Validation, Equity, and Security Testing

Explainability for Public Health AI

References

• A unified approach to interpreting model predictions.Cited in: Explainability for Public Health AI
• Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic ReviewAnna Markella Antoniadi; Yuhan Du; Yasmine Guendouz; et al. 2021. Applied Sciencesjournal articleCited in: Explainability for Public Health AI
• Reinforcement Learning to Prevent Acute Care Events Among Medicaid Populations: Mixed Methods StudySanjay Basu; Bhairavi Muralidharan; Parth Sheth; et al. 2025. JMIR AIjournal articleCited in: Explainability for Public Health AI
• Christoph Molnar, Interpretable Machine Learning (2025)Cited in: Explainability for Public Health AI
• EU AI Act, 2024Cited in: Explainability for Public Health AI
• Exploratory guide to XAI (2019)Cited in: Explainability for Public Health AI
• FDA, 2021 - AI/ML Action PlanCited in: Explainability for Public Health AI
• The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategiesAniek F. Markus; Jan A. Kors; Peter R. Rijnbeek. 2021. Journal of Biomedical Informaticsjournal articleCited in: Explainability for Public Health AI
• "Why Should I Trust You?"Marco Tulio Ribeiro; Sameer Singh; Carlos Guestrin. 2016. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Miningconference paperCited in: Explainability for Public Health AI
• Wachter et al., 2017Cited in: Explainability for Public Health AI

Public Health AI Evaluation Exercises

References

• Miller, 2024, preprintCited in: Public Health AI Evaluation Exercises

Ethics, Bias, and Equity in Healthcare AI

References

• Anthropic, 2025Cited in: Ethics, Bias, and Equity in Healthcare AI
• Reinforcement Learning to Prevent Acute Care Events Among Medicaid Populations: Mixed Methods StudySanjay Basu; Bhairavi Muralidharan; Parth Sheth; et al. 2025. JMIR AIjournal articleCited in: Ethics, Bias, and Equity in Healthcare AI
• Datasheets for DatasetsCited in: Ethics, Bias, and Equity in Healthcare AI (passage 1); Ethics, Bias, and Equity in Healthcare AI (passage 2); Ethics, Bias, and Equity in Healthcare AI (passage 3)
• EESI, 2024Cited in: Ethics, Bias, and Equity in Healthcare AI
• The Missing Dimension in Clinical AI: Making Hidden Values VisibleCarey Goldberg; Ran D. Balicer; Mamatha Bhat; et al. 2026. NEJM AIjournal articleCited in: Ethics, Bias, and Equity in Healthcare AI
• FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcareKarim Lekadir; Alejandro F Frangi; Antonio R Porras; et al. 2025. BMJjournal articleCited in: Ethics, Bias, and Equity in Healthcare AI
• Model Cards for Model ReportingMargaret Mitchell; Simone Wu; Andrew Zaldivar; et al. 2019. Proceedings of the Conference on Fairness, Accountability, and Transparencyconference paperCited in: Ethics, Bias, and Equity in Healthcare AI
• Model CardsCited in: Ethics, Bias, and Equity in Healthcare AI (passage 1); Ethics, Bias, and Equity in Healthcare AI (passage 2)
• Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: Ethics, Bias, and Equity in Healthcare AI (passage 1); Ethics, Bias, and Equity in Healthcare AI (passage 2); Ethics, Bias, and Equity in Healthcare AI (passage 3)Recommended in: Ethics, Bias, and Equity in Healthcare AI (passage 4)
• Vrije Universiteit Amsterdam, 2024Cited in: Ethics, Bias, and Equity in Healthcare AI
• WHO, 2024Cited in: Ethics, Bias, and Equity in Healthcare AI (passage 1); Ethics, Bias, and Equity in Healthcare AI (passage 2)

Recommended reading

• ACM Code of EthicsRecommended in: Ethics, Bias, and Equity in Healthcare AI
• AequitasRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Agarwal et al., 2018 - Reductions approach to fair classificationRecommended in: Ethics, Bias, and Equity in Healthcare AI
• AI EthicsRecommended in: Ethics, Bias, and Equity in Healthcare AI
• AI Fairness 360Recommended in: Ethics, Bias, and Equity in Healthcare AI
• Algorithms of OppressionRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Bellamy et al., 2019 - AI Fairness 360Recommended in: Ethics, Bias, and Equity in Healthcare AI
• Buolamwini & Gebru, 2018 - Gender ShadesRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Chen et al., 2019 - Can AI help reduce disparities in healthcareRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Chouldechova, 2017, Big Data (arXiv preprint)Recommended in: Ethics, Bias, and Equity in Healthcare AI
• Data FeminismRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Data Science EthicsRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Dwork et al., 2012 - Fairness through awarenessRecommended in: Ethics, Bias, and Equity in Healthcare AI
• EU AI ActRecommended in: Ethics, Bias, and Equity in Healthcare AI
• FairlearnRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Fairness in Machine LearningRecommended in: Ethics, Bias, and Equity in Healthcare AI
• FDA, 2021 - AI/ML Action PlanRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Google Model Cards ToolkitRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Hugging Face Model CardsRecommended in: Ethics, Bias, and Equity in Healthcare AI
• IEEE Ethically Aligned DesignRecommended in: Ethics, Bias, and Equity in Healthcare AI
• InterpretMLRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Kamiran & Calders, 2012 - Data preprocessing for discrimination preventionRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Kleinberg et al., 2017, ITCSRecommended in: Ethics, Bias, and Equity in Healthcare AI
• LIMERecommended in: Ethics, Bias, and Equity in Healthcare AI
• Model CardsRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Montreal Declaration for Responsible AIRecommended in: Ethics, Bias, and Equity in Healthcare AI
• ProPublica COMPAS analysisRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Race After TechnologyRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Rajkomar et al., 2018 - Ensuring fairness in ML for healthcareRecommended in: Ethics, Bias, and Equity in Healthcare AI
• SHAPRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Sjoding et al., 2020, NEJMRecommended in: Ethics, Bias, and Equity in Healthcare AI
• Weapons of Math DestructionRecommended in: Ethics, Bias, and Equity in Healthcare AI
• What-If ToolRecommended in: Ethics, Bias, and Equity in Healthcare AI
• WHO Ethics and Governance of AI for HealthRecommended in: Ethics, Bias, and Equity in Healthcare AI

Privacy, Security, and Governance for Health AI

References

• revealed to The Guardian and The New York TimesCited in: Privacy, Security, and Governance for Health AI
• Simple Demographics Often Identify People UniquelySweeney L. 2000. Carnegie Mellon University, Data Privacy Working Paper 3Cited in: Privacy, Security, and Governance for Health AI
• An Investigation of Memorization Risk in Healthcare Foundation ModelsTonekaboni S, Stempfle L, Fallahpour A, et al. 2025. arXivpreprintCited in: Privacy, Security, and Governance for Health AI

Recommended reading

• Abadi et al., 2016 - Deep Learning with Differential PrivacyRecommended in: Privacy, Security, and Governance for Health AI
• AmnesiaRecommended in: Privacy, Security, and Governance for Health AI
• Applied CryptographyRecommended in: Privacy, Security, and Governance for Health AI
• ARX Data Anonymization ToolRecommended in: Privacy, Security, and Governance for Health AI
• Carlini et al., 2019 - The Secret SharerRecommended in: Privacy, Security, and Governance for Health AI
• Cavoukian, 2009 - Privacy by DesignRecommended in: Privacy, Security, and Governance for Health AI
• CCPA/CPRA InformationRecommended in: Privacy, Security, and Governance for Health AI
• CIS ControlsRecommended in: Privacy, Security, and Governance for Health AI
• CTGANRecommended in: Privacy, Security, and Governance for Health AI
• Dwork, 2006 - Differential PrivacyRecommended in: Privacy, Security, and Governance for Health AI
• FATERecommended in: Privacy, Security, and Governance for Health AI
• FDA Guidance on CybersecurityRecommended in: Privacy, Security, and Governance for Health AI
• FlowerRecommended in: Privacy, Security, and Governance for Health AI
• Fredrikson et al., 2015 - Model InversionRecommended in: Privacy, Security, and Governance for Health AI
• GDPR Official TextRecommended in: Privacy, Security, and Governance for Health AI
• Gentry, 2009 - Fully Homomorphic EncryptionRecommended in: Privacy, Security, and Governance for Health AI
• Google DP LibraryRecommended in: Privacy, Security, and Governance for Health AI
• HElibRecommended in: Privacy, Security, and Governance for Health AI
• HIPAA Privacy RuleRecommended in: Privacy, Security, and Governance for Health AI
• HITRUST CSFRecommended in: Privacy, Security, and Governance for Health AI
• ISO/IEC 27001Recommended in: Privacy, Security, and Governance for Health AI
• McMahan et al., 2017 - Federated LearningRecommended in: Privacy, Security, and Governance for Health AI
• Microsoft SEALRecommended in: Privacy, Security, and Governance for Health AI
• Narayanan & Shmatikov, 2008 - Robust De-anonymizationRecommended in: Privacy, Security, and Governance for Health AI
• Nissenbaum, 2004 - Privacy as Contextual IntegrityRecommended in: Privacy, Security, and Governance for Health AI
• NIST Privacy FrameworkRecommended in: Privacy, Security, and Governance for Health AI
• OpenDPRecommended in: Privacy, Security, and Governance for Health AI
• OWASP Top 10Recommended in: Privacy, Security, and Governance for Health AI
• Practical Data PrivacyRecommended in: Privacy, Security, and Governance for Health AI
• Privacy in Statistics and Machine LearningRecommended in: Privacy, Security, and Governance for Health AI
• Privacy is PowerRecommended in: Privacy, Security, and Governance for Health AI
• Programming Differential PrivacyRecommended in: Privacy, Security, and Governance for Health AI
• PySEALRecommended in: Privacy, Security, and Governance for Health AI
• PySyftRecommended in: Privacy, Security, and Governance for Health AI
• sdcMicroRecommended in: Privacy, Security, and Governance for Health AI
• Shokri et al., 2017 - Membership InferenceRecommended in: Privacy, Security, and Governance for Health AI
• SmartNoiseRecommended in: Privacy, Security, and Governance for Health AI
• Sweeney, 2002 - k-AnonymityRecommended in: Privacy, Security, and Governance for Health AI
• Synthetic Data Vault (SDV)Recommended in: Privacy, Security, and Governance for Health AI
• SynthpopRecommended in: Privacy, Security, and Governance for Health AI
• TensorFlow FederatedRecommended in: Privacy, Security, and Governance for Health AI
• The Algorithmic Foundations of Differential PrivacyRecommended in: Privacy, Security, and Governance for Health AI
• The Ethical AlgorithmRecommended in: Privacy, Security, and Governance for Health AI
• Tumult AnalyticsRecommended in: Privacy, Security, and Governance for Health AI

AI Safety in Healthcare: Protecting Patients and Populations

References

• Principles to guide clinical AI readiness and move from benchmarks to real-world evaluationTej D. Azad; Harlan M. Krumholz; Suchi Saria. 2026. Nature Medicinejournal articleCited in: AI Safety in Healthcare: Protecting Patients and Populations
• Ironies of automationLisanne Bainbridge. 1983. Automaticajournal articleCited in: AI Safety in Healthcare: Protecting Patients and Populations
• FDA Recall Z-1734-2021Cited in: AI Safety in Healthcare: Protecting Patients and Populations
• Finlayson et al., 2021, NEJMCited in: AI Safety in Healthcare: Protecting Patients and Populations (passage 1); AI Safety in Healthcare: Protecting Patients and Populations (passage 2)Recommended in: AI Safety in Healthcare: Protecting Patients and Populations (passage 3)
• Goddard et al., 2012, JAMIACited in: AI Safety in Healthcare: Protecting Patients and Populations
• Researchers validate Epic’s sepsis prediction modelCited in: AI Safety in Healthcare: Protecting Patients and Populations (passage 1); AI Safety in Healthcare: Protecting Patients and Populations (passage 2)Recommended in: AI Safety in Healthcare: Protecting Patients and Populations (passage 3)
• Sendak et al., 2020, JMIR Medical InformaticsCited in: AI Safety in Healthcare: Protecting Patients and Populations (passage 1)Recommended in: AI Safety in Healthcare: Protecting Patients and Populations (passage 2)
• Wong et al., 2021Cited in: AI Safety in Healthcare: Protecting Patients and Populations (passage 1); AI Safety in Healthcare: Protecting Patients and Populations (passage 2)
• Zech et al., 2018, PLOS MedicineCited in: AI Safety in Healthcare: Protecting Patients and Populations

Recommended reading

• AI VerifyRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Amodei et al., 2016, Concrete Problems in AI SafetyRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• CleverHansRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Engineering a Safer WorldRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Evidently AIRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Fast.aiRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• FDA, 2021 - AI/ML Action PlanRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• FiddlerRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Finlayson et al., 2019, ScienceRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• FoolboxRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Gerke et al., 2020, The Need for a System View to Regulate AI/ML-Based Medical DevicesRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Introduction to AI Safety, Ethics, and SocietyDan Hendrycks. 2024Recommended in: AI Safety in Healthcare: Protecting Patients and Populations
• IEC 62304:2006+AMD1:2015Recommended in: AI Safety in Healthcare: Protecting Patients and Populations
• IHI FMEA ToolRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• ISO 13485:2016Recommended in: AI Safety in Healthcare: Protecting Patients and Populations
• ISO 14971:2019Recommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Leveson & Turner, 1993, An Investigation of the Therac-25 AccidentsRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Madry et al., 2018, Towards Deep Learning Models Resistant to Adversarial AttacksRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• MDCG 2019-11: Guidance on Qualification and Classification of SoftwareRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Medical Device Regulation (MDR) 2017/745Recommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Medical Device Software Verification, Validation, and ComplianceRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• MIT 6.S191: Introduction to Deep Learning - Safety LectureRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Normal AccidentsRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Parikh et al., 2019, Addressing Bias in AI for Health CareRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Predetermined Change Control Plans for ML-Enabled DevicesRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Risk Register TemplatesRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Safety-Critical Computer SystemsRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Scott et al., 2021, Clinician Checklist for Assessing Suitability of Machine Learning Applications in HealthcareRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Software and AI as a Medical Device Change ProgrammeRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Software as a Medical Device (SaMD): Clinical EvaluationRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• Stanford CS329S: Machine Learning Systems DesignRecommended in: AI Safety in Healthcare: Protecting Patients and Populations
• WhylabsRecommended in: AI Safety in Healthcare: Protecting Patients and Populations

AI Deployment in Healthcare: Why Most Prototypes Fail

References

• A framework for the oversight and local deployment of safe and high-quality prediction modelsArmando D Bedoya; Nicoleta J Economou-Zavlanos; Benjamin A Goldstein; et al. 2022. Journal of the American Medical Informatics Associationjournal articleCited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• CHEERS-AI checklistCited in: AI Deployment in Healthcare: Why Most Prototypes Fail (passage 1); AI Deployment in Healthcare: Why Most Prototypes Fail (passage 2)
• Davis et al., 2017, JAMIACited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• El Arab & Al Moosa, 2025, npj Digital MedicineCited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Elvidge et al., 2024, Value in HealthCited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Finlayson et al., 2021, NEJMCited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Gartner reported in a 2021 cross-industry surveyCited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Gulshan et al., 2016, JAMACited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Advancing healthcare AI governance through a comprehensive maturity model based on systematic reviewRowan Hussein; Anna Zink; Bashar Ramadan; et al. 2026. npj Digital Medicinejournal articleCited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequitiesJee Young Kim; Alifia Hasan; Katherine C. Kellogg; et al. 2024. PLOS Digital Healthjournal articleCited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Krause et al., 2018, OphthalmologyCited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• NIST, 2023Cited in: AI Deployment in Healthcare: Why Most Prototypes Fail (passage 1)Recommended in: AI Deployment in Healthcare: Why Most Prototypes Fail (passage 2)
• NIST, 2026Cited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Rao et al., 2026Cited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Researchers validate Epic’s sepsis prediction modelCited in: AI Deployment in Healthcare: Why Most Prototypes Fail (passage 1); AI Deployment in Healthcare: Why Most Prototypes Fail (passage 2)Recommended in: AI Deployment in Healthcare: Why Most Prototypes Fail (passage 3)
• Sculley et al., 2015, NIPSCited in: AI Deployment in Healthcare: Why Most Prototypes Fail (passage 1)Recommended in: AI Deployment in Healthcare: Why Most Prototypes Fail (passage 2)
• Software as a Medical Device GuidanceCited in: AI Deployment in Healthcare: Why Most Prototypes Fail (passage 1); AI Deployment in Healthcare: Why Most Prototypes Fail (passage 2)
• Wong et al., 2021Cited in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Wynants et al., 2020, BMJCited in: AI Deployment in Healthcare: Why Most Prototypes Fail

Recommended reading

• ArizeRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Benjamens et al., 2020, npj Digital Medicine - The state of AI-based FDA-approved medical devicesRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Breck et al., 2017, IEEE Big Data - The ML test score: A rubric for ML production readinessRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Building Machine Learning Powered ApplicationsRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Designing Data-Intensive ApplicationsRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• DockerRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• DVCRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Evidently AIRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• FastAPIRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• FDA CDS Guidance, Jan 2026Recommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• FDA, 2021 - AI/ML Action PlanRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• FHIR ClientRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• FiddlerRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Full Stack Deep LearningRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Gama et al., 2014, ACM Computing Surveys - A survey on concept drift adaptationRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Good Machine Learning Practice for Medical Device DevelopmentRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Google’s ML Engineering Best PracticesRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• GrafanaRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• HAPI FHIRRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• HL7apyRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• KubeflowRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Kubeflow 101Recommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• KubernetesRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Lu et al., 2019, IEEE Transactions on Knowledge and Data Engineering - Learning under concept drift: A reviewRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Machine Learning Design PatternsRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Made With ML - MLOpsRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Microsoft’s Responsible AI GuidelinesRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• MLflowRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• MLOps SpecializationRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• PrometheusRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Rabanser et al., 2019, NeurIPS - Failing loudly: An empirical study of methods for detecting dataset shiftRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Rajkomar et al., 2018, npj Digital Medicine - Scalable and accurate deep learning for EHRRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Reliable Machine LearningRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Sato et al., 2019, IEEE Software - Continuous delivery for machine learningRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Seldon CoreRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Sendak et al., 2020, JMIR Medical InformaticsRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Site Reliability EngineeringRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail
• Weights & BiasesRecommended in: AI Deployment in Healthcare: Why Most Prototypes Fail

MLOps for Public Health AI

References

• Alla & Adari, 2021, Beginning MLOps with MLflowCited in: MLOps for Public Health AI
• Humans or LLMs as the Judge? A Study on Judgement BiasGuiming Hardy Chen; Shunian Chen; Ziche Liu; et al. 2024. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processingconference paperCited in: MLOps for Public Health AI
• DVCCited in: MLOps for Public Health AI
• Kreuzberger et al., 2023, IEEE AccessCited in: MLOps for Public Health AI
• LakeFSCited in: MLOps for Public Health AI
• MLflowCited in: MLOps for Public Health AI
• NIST AI 800-2, initial public draft, 2026Cited in: MLOps for Public Health AI
• Sato et al., 2019, IEEE Software - Continuous delivery for machine learningCited in: MLOps for Public Health AI
• Treveil et al., 2020, Introducing MLOpsCited in: MLOps for Public Health AI
• Weights & BiasesCited in: MLOps for Public Health AI
• Yao et al., 2025Cited in: MLOps for Public Health AI
• Zaharia et al., 2018, IEEE Data Eng. Bull.Cited in: MLOps for Public Health AI
• Zhou et al., 2020, IEEE ICAICECited in: MLOps for Public Health AI

Recommended reading

• Life Sciences AI Handbook agentic workflows chapterRecommended in: MLOps for Public Health AI
• MLflow documentationRecommended in: MLOps for Public Health AI

Deployment and Production Monitoring

References

• Baylor et al., 2017, KDDCited in: Deployment and Production Monitoring
• Breck et al., 2017, IEEE Big Data - The ML test score: A rubric for ML production readinessCited in: Deployment and Production Monitoring
• Fowler, 2010 - BlueGreenDeploymentCited in: Deployment and Production Monitoring
• Gama et al., 2014, ACM Computing Surveys - A survey on concept drift adaptationCited in: Deployment and Production Monitoring
• Howard et al., 2017, arXivCited in: Deployment and Production Monitoring
• Humble & Farley, 2010, Continuous DeliveryCited in: Deployment and Production Monitoring
• Lu et al., 2018, ACM Computing SurveysCited in: Deployment and Production Monitoring
• PrometheusCited in: Deployment and Production Monitoring
• Rabanser et al., 2019, NeurIPS - Failing loudly: An empirical study of methods for detecting dataset shiftCited in: Deployment and Production Monitoring
• Richardson, 2018, Microservices PatternsCited in: Deployment and Production Monitoring

System Integration and Regulatory Compliance

References

• Benjamens et al., 2020, npj Digital Medicine - The state of AI-based FDA-approved medical devicesCited in: System Integration and Regulatory Compliance
• FDA CDS Guidance, Jan 2026Cited in: System Integration and Regulatory Compliance
• FDA Post-Market Surveillance guidanceCited in: System Integration and Regulatory Compliance
• HL7 FHIR SpecificationCited in: System Integration and Regulatory Compliance
• HL7 Version 2 Product SuiteCited in: System Integration and Regulatory Compliance
• Mandel et al., 2016, Journal of the American Medical Informatics AssociationCited in: System Integration and Regulatory Compliance
• Software as a Medical Device GuidanceCited in: System Integration and Regulatory Compliance

AI in Public Health Emergency Operations

References

• Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparednessAnshu Ankolekar. 2024. Computational and Structural Biotechnology Journaljournal articleCited in: AI in Public Health Emergency Operations
• Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triageMingyang Chen; Yijin Wu; Jiayi Ma; et al. 2026. npj Digital Medicinejournal articleCited in: AI in Public Health Emergency Operations
• FDA, 2024Cited in: AI in Public Health Emergency Operations
• FDA, accessed August 2026Cited in: AI in Public Health Emergency Operations
• U.S. Fire Administration, 2026Cited in: AI in Public Health Emergency Operations
• WHO, 2015Cited in: AI in Public Health Emergency Operations
• WHO, 2026Cited in: AI in Public Health Emergency Operations
• WHO, 2026Cited in: AI in Public Health Emergency Operations

Recommended reading

• Infectious Diseases and Antimicrobial StewardshipRecommended in: AI in Public Health Emergency Operations
• Medical CountermeasuresRecommended in: AI in Public Health Emergency Operations

Part IV: Practical Tools and Resources

Your AI Toolkit for Public Health

References

• EpiNow2Cited in: Your AI Toolkit for Public Health
• ExecuTorchCited in: Your AI Toolkit for Public Health
• FDA Part 11 guidanceCited in: Your AI Toolkit for Public Health
• Google Colab FAQCited in: Your AI Toolkit for Public Health (passage 1); Your AI Toolkit for Public Health (passage 2); Your AI Toolkit for Public Health (passage 3)
• Johns Hopkins CRC FAQCited in: Your AI Toolkit for Public Health
• Polars documentationCited in: Your AI Toolkit for Public Health
• RuffCited in: Your AI Toolkit for Public Health
• uvCited in: Your AI Toolkit for Public Health
• uvCited in: Your AI Toolkit for Public Health

Recommended reading

• 10 minutes to pandasRecommended in: Your AI Toolkit for Public Health
• Andrew Ng’s Machine Learning SpecializationRecommended in: Your AI Toolkit for Public Health
• Choosing the right estimatorRecommended in: Your AI Toolkit for Public Health
• Deep Learning SpecializationRecommended in: Your AI Toolkit for Public Health
• Deep Learning with PythonRecommended in: Your AI Toolkit for Public Health
• Fast.aiRecommended in: Your AI Toolkit for Public Health
• Full Stack Deep LearningRecommended in: Your AI Toolkit for Public Health
• Geron’s practical guideRecommended in: Your AI Toolkit for Public Health
• Google Colab TutorialsRecommended in: Your AI Toolkit for Public Health
• Kaggle LearnRecommended in: Your AI Toolkit for Public Health
• MatplotlibRecommended in: Your AI Toolkit for Public Health
• MLflow documentationRecommended in: Your AI Toolkit for Public Health (passage 1); Your AI Toolkit for Public Health (passage 2)
• NumPyRecommended in: Your AI Toolkit for Public Health
• pandasRecommended in: Your AI Toolkit for Public Health
• pandas documentationRecommended in: Your AI Toolkit for Public Health (passage 1); Your AI Toolkit for Public Health (passage 2)
• Python Data Science HandbookRecommended in: Your AI Toolkit for Public Health
• PyTorch documentationRecommended in: Your AI Toolkit for Public Health (passage 1); Your AI Toolkit for Public Health (passage 2)
• PyTorch tutorialsRecommended in: Your AI Toolkit for Public Health
• scikit-learnRecommended in: Your AI Toolkit for Public Health
• scikit-learn documentationRecommended in: Your AI Toolkit for Public Health (passage 1); Your AI Toolkit for Public Health (passage 2)

Building Your First Public Health AI Project

References

• Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost PatientsDavid W. Bates; Suchi Saria; Lucila Ohno-Machado; et al. 2014. Health Affairsjournal articleCited in: Building Your First Public Health AI Project
• Bergstra & Bengio, 2012, JMLRCited in: Building Your First Public Health AI Project
• Caruana et al., 2015, KDDCited in: Building Your First Public Health AI Project (passage 1)Recommended in: Building Your First Public Health AI Project (passage 2)
• XGBoostTianqi Chen; Carlos Guestrin. 2016. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Miningconference paperCited in: Building Your First Public Health AI Project
• CrowdFlower, 2016 surveyCited in: Building Your First Public Health AI Project
• Feldman et al., 2015, KDDCited in: Building Your First Public Health AI Project (passage 1)Recommended in: Building Your First Public Health AI Project (passage 2)
• Flexible Imputation of Missing DataCited in: Building Your First Public Health AI Project
• Holstein et al., 2019, CHICited in: Building Your First Public Health AI Project
• https://archive.ics.uci.edu/dataset/296/diabetes+130-us+hospitals+for+years+1999-2008Cited in: Building Your First Public Health AI Project
• https://physionet.org/content/mimiciii/Cited in: Building Your First Public Health AI Project (passage 1); Building Your First Public Health AI Project (passage 2)
• Rehospitalizations among Patients in the Medicare Fee-for-Service ProgramStephen F. Jencks; Mark V. Williams; Eric A. Coleman. 2009. New England Journal of Medicinejournal articleCited in: Building Your First Public Health AI Project
• Jencks et al., 2009, NEJMCited in: Building Your First Public Health AI Project
• Johnson et al., 2016, Scientific DataCited in: Building Your First Public Health AI Project
• Kansagara et al., 2011, JAMACited in: Building Your First Public Health AI Project (passage 1)Recommended in: Building Your First Public Health AI Project (passage 2)
• Lundberg & Lee, 2017, NIPSCited in: Building Your First Public Health AI Project (passage 1)Recommended in: Building Your First Public Health AI Project (passage 2)
• Ng, 2021, MLOps lectureCited in: Building Your First Public Health AI Project (passage 1); Building Your First Public Health AI Project (passage 2)
• Saito & Rehmsmeier, 2015, PLOS ONECited in: Building Your First Public Health AI Project
• Sato et al., 2019, IEEE Software - Continuous delivery for machine learningCited in: Building Your First Public Health AI Project (passage 1)Recommended in: Building Your First Public Health AI Project (passage 2)
• Sendak et al., 2020, npj Digital MedicineCited in: Building Your First Public Health AI Project
• StreamlitCited in: Building Your First Public Health AI Project

Recommended reading

• Alliance for AI in Healthcare (AAIH)Recommended in: Building Your First Public Health AI Project
• Andrew Ng’s Machine Learning SpecializationRecommended in: Building Your First Public Health AI Project
• AWS Machine Learning Engineer NanodegreeRecommended in: Building Your First Public Health AI Project
• Breck et al., 2017, IEEE Big Data - The ML test score: A rubric for ML production readinessRecommended in: Building Your First Public Health AI Project
• Building Machine Learning Powered ApplicationsRecommended in: Building Your First Public Health AI Project
• Chen et al., 2019, FAccT/FAT*Recommended in: Building Your First Public Health AI Project
• Clinical Natural Language ProcessingRecommended in: Building Your First Public Health AI Project
• Clinical Prediction ModelsRecommended in: Building Your First Public Health AI Project
• CMS Hospital ReadmissionsRecommended in: Building Your First Public Health AI Project
• Cookiecutter Data ScienceRecommended in: Building Your First Public Health AI Project
• Data Science for BusinessRecommended in: Building Your First Public Health AI Project
• Deep Learning SpecializationRecommended in: Building Your First Public Health AI Project
• eICURecommended in: Building Your First Public Health AI Project
• Fast.aiRecommended in: Building Your First Public Health AI Project
• Fast.ai: Practical Deep Learning for CodersRecommended in: Building Your First Public Health AI Project
• Full Stack Deep LearningRecommended in: Building Your First Public Health AI Project
• A comparison of models for predicting early hospital readmissionsJoseph Futoma; Jonathan Morris; Joseph Lucas. 2015. Journal of Biomedical Informaticsjournal articleRecommended in: Building Your First Public Health AI Project
• Google Colab TutorialsRecommended in: Building Your First Public Health AI Project
• Health Informatics on FHIRRecommended in: Building Your First Public Health AI Project
• Health Informatics SpecializationRecommended in: Building Your First Public Health AI Project
• Healthcare Data AnalyticsRecommended in: Building Your First Public Health AI Project
• HL7 FHIR SpecificationRecommended in: Building Your First Public Health AI Project
• Kaggle ForumsRecommended in: Building Your First Public Health AI Project
• Kaggle Healthcare DatasetsRecommended in: Building Your First Public Health AI Project
• Kaggle LearnRecommended in: Building Your First Public Health AI Project
• Machine Learning for HealthcareRecommended in: Building Your First Public Health AI Project
• Made With ML - MLOpsRecommended in: Building Your First Public Health AI Project
• MIMIC-III TutorialsRecommended in: Building Your First Public Health AI Project
• MIT Machine Learning for Healthcare (6.7930/HST.956)Recommended in: Building Your First Public Health AI Project
• MLOps CommunityRecommended in: Building Your First Public Health AI Project (passage 1); Building Your First Public Health AI Project (passage 2)
• MLOps SpecializationRecommended in: Building Your First Public Health AI Project
• Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleRecommended in: Building Your First Public Health AI Project
• PhysioNetRecommended in: Building Your First Public Health AI Project
• r/datascienceRecommended in: Building Your First Public Health AI Project
• r/MachineLearningRecommended in: Building Your First Public Health AI Project
• Rajkomar et al., 2018, npj Digital Medicine - Scalable and accurate deep learning for EHRRecommended in: Building Your First Public Health AI Project
• "Why Should I Trust You?"Marco Tulio Ribeiro; Sameer Singh; Carlos Guestrin. 2016. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Miningconference paperRecommended in: Building Your First Public Health AI Project
• Sculley et al., 2015, NIPSRecommended in: Building Your First Public Health AI Project
• TensorFlow: Data and DeploymentRecommended in: Building Your First Public Health AI Project
• UCI ML Repository - MedicalRecommended in: Building Your First Public Health AI Project

AI-Assisted Coding for Public Health Analysis

References

• Chen et al., 2021, arXivCited in: AI-Assisted Coding for Public Health Analysis (passage 1)Recommended in: AI-Assisted Coding for Public Health Analysis (passage 2)
• CursorCited in: AI-Assisted Coding for Public Health Analysis
• Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural LanguagePaul Denny; Viraj Kumar; Nasser Giacaman. 2023. Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1conference paperCited in: AI-Assisted Coding for Public Health Analysis
• Dohmke, GitHub, June 2023Cited in: AI-Assisted Coding for Public Health Analysis
• GitHub Copilot documentationCited in: AI-Assisted Coding for Public Health Analysis (passage 1); AI-Assisted Coding for Public Health Analysis (passage 2)
• Kluyver et al., 2016, IOS PressCited in: AI-Assisted Coding for Public Health Analysis
• METR, 2025Cited in: AI-Assisted Coding for Public Health Analysis
• Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code ContributionsHammond Pearce; Baleegh Ahmad; Benjamin Tan; et al. 2022. 2022 IEEE Symposium on Security and Privacy (SP)conference paperCited in: AI-Assisted Coding for Public Health Analysis (passage 1); AI-Assisted Coding for Public Health Analysis (passage 2); AI-Assisted Coding for Public Health Analysis (passage 3); AI-Assisted Coding for Public Health Analysis (passage 4)
• Peng et al., 2023, preprintCited in: AI-Assisted Coding for Public Health Analysis (passage 1); AI-Assisted Coding for Public Health Analysis (passage 2); AI-Assisted Coding for Public Health Analysis (passage 3)
• Posit documentationCited in: AI-Assisted Coding for Public Health Analysis
• RStudio: A Platform‐Independent IDE for R and SweaveJeffrey S. Racine. 2012. Journal of Applied Econometricsjournal articleCited in: AI-Assisted Coding for Public Health Analysis
• Replit pricingCited in: AI-Assisted Coding for Public Health Analysis
• Attitudes Towards the Use (and Misuse) of ChatGPT: A Preliminary StudyMichael P. Rogers; Hannah Miller Hillberg; Christopher L. Groves. 2024. Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1conference paperCited in: AI-Assisted Coding for Public Health Analysis
• Ten simple rules for writing and sharing computational analyses in Jupyter NotebooksAdam Rule; Amanda Birmingham; Cristal Zuniga; et al. 2019. PLOS Computational Biologyjournal articleCited in: AI-Assisted Coding for Public Health Analysis
• Sourcegraph, 2025Cited in: AI-Assisted Coding for Public Health Analysis
• Stack Overflow, 2023Cited in: AI-Assisted Coding for Public Health Analysis

Recommended reading

• CDC Data Academy R TrainingRecommended in: AI-Assisted Coding for Public Health Analysis
• EpiEstim PackageRecommended in: AI-Assisted Coding for Public Health Analysis
• GitHub Copilot plans and pricingRecommended in: AI-Assisted Coding for Public Health Analysis
• GitHub Copilot ResearchRecommended in: AI-Assisted Coding for Public Health Analysis
• https://code.visualstudio.com/Recommended in: AI-Assisted Coding for Public Health Analysis
• Jupyter TutorialRecommended in: AI-Assisted Coding for Public Health Analysis
• official plan pageRecommended in: AI-Assisted Coding for Public Health Analysis
• Posit RecipesRecommended in: AI-Assisted Coding for Public Health Analysis
• Pro Git BookRecommended in: AI-Assisted Coding for Public Health Analysis
• Prompt Engineering for CodeRecommended in: AI-Assisted Coding for Public Health Analysis
• Public Health Data Science CourseRecommended in: AI-Assisted Coding for Public Health Analysis
• Python for Data AnalysisRecommended in: AI-Assisted Coding for Public Health Analysis
• R for Data ScienceRecommended in: AI-Assisted Coding for Public Health Analysis
• Software CarpentryRecommended in: AI-Assisted Coding for Public Health Analysis
• VS Code DocumentationRecommended in: AI-Assisted Coding for Public Health Analysis

Part V: The Future

Emerging AI Technologies for Public Health

References

• 3+ billion WhatsApp usersCited in: Emerging AI Technologies for Public Health
• Abadie & Gardeazabal, 2003, American Economic ReviewCited in: Emerging AI Technologies for Public Health
• Artificial intelligence for climate–health early warning systems in the Horn of Africa: opportunities, challenges, and a roadmap for actionAhmed Abdiaziz Alasow; Yusuf Hared Abdi; Abdifatah Ahmed Hersi; et al. 2026. Globalization and Healthjournal articleCited in: Emerging AI Technologies for Public Health
• AlphaFold DBCited in: Emerging AI Technologies for Public Health
• Burger et al., 2020, NatureCited in: Emerging AI Technologies for Public Health
• Cao et al., 2018, Chemical ReviewsCited in: Emerging AI Technologies for Public Health
• CDC’s investigation frameworkCited in: Emerging AI Technologies for Public Health
• DeepSeek Deployed in 90 Chinese Tertiary Hospitals: How Artificial Intelligence Is Transforming Clinical PracticeJishizhan Chen; Chunying Miao. 2025. Journal of Medical Systemsjournal articleCited in: Emerging AI Technologies for Public Health
• Corso et al., 2023, ICLR - DiffDockCited in: Emerging AI Technologies for Public Health
• On the Integration of Agents and Digital Twins in HealthcareAngelo Croatti; Matteo Gabellini; Sara Montagna; et al. 2020. Journal of Medical Systemsjournal articleCited in: Emerging AI Technologies for Public Health
• D-Wave systemsCited in: Emerging AI Technologies for Public Health
• Innovation in the pharmaceutical industry: New estimates of R&D costsJoseph A. DiMasi; Henry G. Grabowski; Ronald W. Hansen. 2016. Journal of Health Economicsjournal articleCited in: Emerging AI Technologies for Public Health
• EPA AirNowCited in: Emerging AI Technologies for Public Health
• FlowerCited in: Emerging AI Technologies for Public Health (passage 1); Emerging AI Technologies for Public Health (passage 2)
• Artificial Intelligence in Drug Discovery and Development: Raising Quality per DecisionShota Furukawa; Hiroyuki Uchida; Taishiro Kishimoto. 2026. Pharmacopsychiatryjournal articleCited in: Emerging AI Technologies for Public Health (passage 1); Emerging AI Technologies for Public Health (passage 2)
• Google Air Quality APICited in: Emerging AI Technologies for Public Health
• Jackson et al., 2020Cited in: Emerging AI Technologies for Public Health
• Drug discovery with explainable artificial intelligenceJosé Jiménez-Luna; Francesca Grisoni; Gisbert Schneider. 2020. Nature Machine Intelligencejournal articleCited in: Emerging AI Technologies for Public Health
• Jumper et al., 2021, NatureCited in: Emerging AI Technologies for Public Health (passage 1)Recommended in: Emerging AI Technologies for Public Health (passage 2)
• Kleinig et al., 2024, Internal Medicine JournalCited in: Emerging AI Technologies for Public Health
• LiteRT guidanceCited in: Emerging AI Technologies for Public Health
• Luo et al., 2022, Briefings in BioinformaticsCited in: Emerging AI Technologies for Public Health
• DeepTox: Toxicity Prediction using Deep LearningAndreas Mayr; Günter Klambauer; Thomas Unterthiner; et al. 2016. Frontiers in Environmental Sciencejournal articleCited in: Emerging AI Technologies for Public Health
• McMahan & Ramage, 2017, Google AI BlogCited in: Emerging AI Technologies for Public Health
• McMahan et al., 2017 - Federated LearningCited in: Emerging AI Technologies for Public Health (passage 1)Recommended in: Emerging AI Technologies for Public Health (passage 2)
• Med-PaLM 2Cited in: Emerging AI Technologies for Public Health
• medRxiv, 2025Cited in: Emerging AI Technologies for Public Health
• Mishra et al., 2020, Nature Biomedical EngineeringCited in: Emerging AI Technologies for Public Health
• Modi et al., 2025, Annals of Internal MedicineCited in: Emerging AI Technologies for Public Health
• Monitoring zoonotic disease emergenceCited in: Emerging AI Technologies for Public Health
• Multi-Partner LearningCited in: Emerging AI Technologies for Public Health
• Scaling digital twins from the artisanal to the industrialSteven A. Niederer; Michael S. Sacks; Mark Girolami; et al. 2021. Nature Computational Sciencejournal articleCited in: Emerging AI Technologies for Public Health (passage 1); Emerging AI Technologies for Public Health (passage 2)
• NIH, 2020Cited in: Emerging AI Technologies for Public Health
• NIST AI Risk Management FrameworkCited in: Emerging AI Technologies for Public Health
• Orús et al., 2018, arXivCited in: Emerging AI Technologies for Public Health
• Pearl & Mackenzie, 2018, The Book of WhyCited in: Emerging AI Technologies for Public Health (passage 1)Recommended in: Emerging AI Technologies for Public Health (passage 2)
• Pearl, 2009, CausalityCited in: Emerging AI Technologies for Public Health (passage 1)Recommended in: Emerging AI Technologies for Public Health (passage 2)
• Quantum Computing in the NISQ era and beyondJohn Preskill. 2018. Quantumjournal articleCited in: Emerging AI Technologies for Public Health (passage 1); Emerging AI Technologies for Public Health (passage 2)
• PurpleAirCited in: Emerging AI Technologies for Public Health
• Rajaraman et al., 2018, PeerJCited in: Emerging AI Technologies for Public Health
• A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical modelsFeng Ren; Alex Aliper; Jian Chen; et al. 2024. Nature Biotechnologyjournal articleCited in: Emerging AI Technologies for Public Health
• The future of digital health with federated learningNicola Rieke; Jonny Hancox; Wenqi Li; et al. 2020. npj Digital Medicinejournal articleCited in: Emerging AI Technologies for Public Health
• Benchmark evaluation of DeepSeek large language models in clinical decision-makingSarah Sandmann; Stefan Hegselmann; Michael Fujarski; et al. 2025. Nature Medicinejournal articleCited in: Emerging AI Technologies for Public Health
• Sheller et al., 2020, Scientific ReportsCited in: Emerging AI Technologies for Public Health
• Regulating the AI-enabled ecosystem for human therapeuticsRominder Singh; Mark Paxton; Jared Auclair. 2025. Communications Medicinejournal articleCited in: Emerging AI Technologies for Public Health
• Large language models encode clinical knowledgeKaran Singhal; Shekoofeh Azizi; Tao Tu; et al. 2023. Naturejournal articleCited in: Emerging AI Technologies for Public Health (passage 1)Recommended in: Emerging AI Technologies for Public Health (passage 2)
• Spirtes et al., 2000, Causation, Prediction, and SearchCited in: Emerging AI Technologies for Public Health
• Stilgoe et al., 2013, Research PolicyCited in: Emerging AI Technologies for Public Health
• Stärk et al., 2022, ICML - EquiBindCited in: Emerging AI Technologies for Public Health
• Taylor et al., 2022, arXivCited in: Emerging AI Technologies for Public Health
• A spatiotemporal U-Net++ deep learning framework for dengue risk mapping in ColombiaDaira Velandia; Javiera Contador; Juan Zamora; et al. 2026. Scientific Reportsjournal articleCited in: Emerging AI Technologies for Public Health
• Virtual SingaporeCited in: Emerging AI Technologies for Public Health
• WhatsApp Business APICited in: Emerging AI Technologies for Public Health
• WhatsApp Business PlatformCited in: Emerging AI Technologies for Public Health
• WhatsApp Cloud APICited in: Emerging AI Technologies for Public Health
• WHO, 2024Cited in: Emerging AI Technologies for Public Health (passage 1); Emerging AI Technologies for Public Health (passage 2)
• Yang et al., JAMA Network Open, 2024Cited in: Emerging AI Technologies for Public Health

Recommended reading

• A Crash Course in CausalityRecommended in: Emerging AI Technologies for Public Health
• AI for GoodRecommended in: Emerging AI Technologies for Public Health
• Bommasani et al., 2021, arXivRecommended in: Emerging AI Technologies for Public Health
• Causal Inference: The MixtapeRecommended in: Emerging AI Technologies for Public Health
• CausalNexRecommended in: Emerging AI Technologies for Public Health
• CDC Climate and HealthRecommended in: Emerging AI Technologies for Public Health
• Climate Change AIRecommended in: Emerging AI Technologies for Public Health
• Core MLRecommended in: Emerging AI Technologies for Public Health
• DoWhyRecommended in: Emerging AI Technologies for Public Health
• EconMLRecommended in: Emerging AI Technologies for Public Health
• Fast.aiRecommended in: Emerging AI Technologies for Public Health
• Flower CommunityRecommended in: Emerging AI Technologies for Public Health
• Healthcare AI Ethics CommunityRecommended in: Emerging AI Technologies for Public Health
• Hugging Face TransformersRecommended in: Emerging AI Technologies for Public Health
• Introduction to Causal InferenceRecommended in: Emerging AI Technologies for Public Health
• Kaissis et al., 2020, Nature Machine IntelligenceRecommended in: Emerging AI Technologies for Public Health
• Künzel et al., 2019, PNASRecommended in: Emerging AI Technologies for Public Health
• Lancet Countdown on Health and Climate ChangeRecommended in: Emerging AI Technologies for Public Health
• LangChainRecommended in: Emerging AI Technologies for Public Health
• Life 3.0Recommended in: Emerging AI Technologies for Public Health
• ML4H (Machine Learning for Health)Recommended in: Emerging AI Technologies for Public Health
• ONNX RuntimeRecommended in: Emerging AI Technologies for Public Health
• OpenAI GPT-4 APIRecommended in: Emerging AI Technologies for Public Health
• OpenMined CommunityRecommended in: Emerging AI Technologies for Public Health
• Pearl, 2019, Communications of the ACMRecommended in: Emerging AI Technologies for Public Health
• Prediction MachinesRecommended in: Emerging AI Technologies for Public Health
• PySyftRecommended in: Emerging AI Technologies for Public Health
• Quantum Machine LearningRecommended in: Emerging AI Technologies for Public Health
• Stanford CS324: Large Language ModelsRecommended in: Emerging AI Technologies for Public Health
• Stokes et al., 2020, CellRecommended in: Emerging AI Technologies for Public Health
• TensorFlow FederatedRecommended in: Emerging AI Technologies for Public Health
• TensorFlow LiteRecommended in: Emerging AI Technologies for Public Health
• The Master AlgorithmRecommended in: Emerging AI Technologies for Public Health
• WHO Climate Change and HealthRecommended in: Emerging AI Technologies for Public Health

AI in Global Health and Equity

References

• Anthropic, 2025Cited in: AI in Global Health and Equity
• Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series AnalysisIris Ganser; Rodolphe Thiébaut; David L Buckeridge. 2022. JMIR Public Health and Surveillancejournal articleCited in: AI in Global Health and Equity
• ITU, 2025Cited in: AI in Global Health and Equity
• Impact of LLM assistance on physician decision-making: a multi-country randomized controlled trialNicholas Rounding; Luthfi Saiful Arif; Janine Berg; et al. 2026. npj Digital Medicinejournal articleCited in: AI in Global Health and Equity
• Stürenburg et al., 2026Cited in: AI in Global Health and Equity
• WHO Ethics and Governance of AI for HealthCited in: AI in Global Health and Equity
• World Bank, 2026Cited in: AI in Global Health and Equity
• World Health OrganizationCited in: AI in Global Health and Equity (passage 1); AI in Global Health and Equity (passage 2)

Recommended reading

• Agarwal et al. (2018). A reductions approach to fair classification. ICMLRecommended in: AI in Global Health and Equity
• Freely available on GitHubRecommended in: AI in Global Health and Equity
• Gichoya et al., 2022, Lancet Digital HealthRecommended in: AI in Global Health and Equity
• Gulshan et al., 2016, JAMARecommended in: AI in Global Health and Equity
• Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 systemKeelin Murphy; Shifa Salman Habib; Syed Mohammad Asad Zaidi; et al. 2020. Scientific Reportsjournal articleRecommended in: AI in Global Health and Equity
• Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleRecommended in: AI in Global Health and Equity
• Sjoding et al., 2020, NEJMRecommended in: AI in Global Health and Equity
• Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood SmearsFeng Yang; Mahdieh Poostchi; Hang Yu; et al. 2020. IEEE Journal of Biomedical and Health Informaticsjournal articleRecommended in: AI in Global Health and Equity

Context-Appropriate Global Health AI

References

• 2024 systematic reviewCited in: Context-Appropriate Global Health AI
• DHIS2Cited in: Context-Appropriate Global Health AI
• Federated learningCited in: Context-Appropriate Global Health AI
• Google, 2024Cited in: Context-Appropriate Global Health AI
• Gulshan et al., 2016, JAMACited in: Context-Appropriate Global Health AI
• Global approaches to infectious disease surveillance and modelingMark P. Khurana; Joseph L.-H. Tsui; Bernardo Gutierrez; et al. 2026. Nature Medicinejournal articleCited in: Context-Appropriate Global Health AI
• Knowledge distillationCited in: Context-Appropriate Global Health AI
• Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 systemKeelin Murphy; Shifa Salman Habib; Syed Mohammad Asad Zaidi; et al. 2020. Scientific Reportsjournal articleCited in: Context-Appropriate Global Health AI
• Murphy et al., 2020Cited in: Context-Appropriate Global Health AI
• Strengthening Immunisation Data Systems: a mixed-method evaluation of the Lao Electronic Immunisation RegistryCyra Patel; Praveena Gunaratnam; Gemma Saravanos; et al. 2026. npj Digital Public Healthjournal articleCited in: Context-Appropriate Global Health AI
• UNICEF UgandaCited in: Context-Appropriate Global Health AI
• Vital Strategies, February 2026Cited in: Context-Appropriate Global Health AI (passage 1); Context-Appropriate Global Health AI (passage 2)
• WHO & IndiaAI, AI Health Casebook, 2026Cited in: Context-Appropriate Global Health AI
• Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood SmearsFeng Yang; Mahdieh Poostchi; Hang Yu; et al. 2020. IEEE Journal of Biomedical and Health Informaticsjournal articleCited in: Context-Appropriate Global Health AI

Recommended reading

• DHIS2 AcademyRecommended in: Context-Appropriate Global Health AI
• DHIS2 CommunityRecommended in: Context-Appropriate Global Health AI
• DHIS2 Developer PortalRecommended in: Context-Appropriate Global Health AI

Global Health AI Equity and Local Capacity

References

• Disparities in dermatology AI performance on a diverse, curated clinical image setRoxana Daneshjou; Kailas Vodrahalli; Roberto A. Novoa; et al. 2022. Science Advancesjournal articleCited in: Global Health AI Equity and Local Capacity
• Gichoya et al., 2022, Lancet Digital HealthCited in: Global Health AI Equity and Local Capacity
• ILO, 2025Cited in: Global Health AI Equity and Local Capacity
• Patterns of online consultation use in Great Britain, 2019–2023: an observational analysisGabriele Kerr; Geva Greenfield; Alex Bottle; et al. 2025. BMJ Digital Health & AIjournal articleCited in: Global Health AI Equity and Local Capacity
• Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: Global Health AI Equity and Local Capacity
• Sjoding et al., 2020, NEJMCited in: Global Health AI Equity and Local Capacity

Global Health AI Data Governance

References

• afrimedqa.comCited in: Global Health AI Data Governance
• Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trialAmbrose Agweyu; Paul Mwaniki; Vaishnavi Menon; et al. 2026. Nature Medicinejournal articleCited in: Global Health AI Data Governance
• Barron et al., 2018Cited in: Global Health AI Data Governance
• Chen et al., Journal of Medical Systems, 2025Cited in: Global Health AI Data Governance
• Gabriel and Olawuyi, 2026, preprintCited in: Global Health AI Data Governance
• Gabriel et al., 2026, preprintCited in: Global Health AI Data Governance
• Gates Foundation, 2024-2026Cited in: Global Health AI Data Governance
• Global trends in emerging infectious diseasesKate E. Jones; Nikkita G. Patel; Marc A. Levy; et al. 2008. Naturejournal articleCited in: Global Health AI Data Governance
• Lancet Global Health, ongoingCited in: Global Health AI Data Governance
• Li et al., Nature Medicine, 2024Cited in: Global Health AI Data Governance
• National One Health Framework to Address Zoonotic DiseasesCited in: Global Health AI Data Governance (passage 1); Global Health AI Data Governance (passage 2)
• AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark DatasetCharles Nimo; Tobi Olatunji; Abraham Toluwase Owodunni; et al. 2025. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)conference paperCited in: Global Health AI Data Governance
• Ong et al., Nature Health, 2026Cited in: Global Health AI Data Governance (passage 1); Global Health AI Data Governance (passage 2)
• WHO, May 2025Cited in: Global Health AI Data Governance
• WHO, May 2026Cited in: Global Health AI Data Governance
• WHO/ITU/WIPO, 2023Cited in: Global Health AI Data Governance
• Wong et al., JAMA, 2025Cited in: Global Health AI Data Governance

Recommended reading

• Biosecurity HandbookRecommended in: Global Health AI Data Governance
• CDC One HealthRecommended in: Global Health AI Data Governance
• Federal One Health CoordinationRecommended in: Global Health AI Data Governance
• U.S. One Health Zoonotic Disease PrioritizationRecommended in: Global Health AI Data Governance

AI Policy and Governance in Healthcare

References

• Adapted from IIA, 2020Cited in: AI Policy and Governance in Healthcare (passage 1)Recommended in: AI Policy and Governance in Healthcare (passage 2)
• AI strategyCited in: AI Policy and Governance in Healthcare (passage 1); AI Policy and Governance in Healthcare (passage 2); AI Policy and Governance in Healthcare (passage 3)
• Artificial intelligence and evidence-informed policy: emerging challenges and opportunitiesCited in: AI Policy and Governance in Healthcare
• Balkin, 2017, Ohio State Law JournalCited in: AI Policy and Governance in Healthcare (passage 1)Recommended in: AI Policy and Governance in Healthcare (passage 2)
• Benjamens et al., 2020Cited in: AI Policy and Governance in Healthcare
• A Licensure Framework for Autonomous Clinical AIAlon Bergman; Robert M. Wachter; Ezekiel J. Emanuel. 2026. JAMAjournal articleCited in: AI Policy and Governance in Healthcare
• Caruana et al., 2015, KDDCited in: AI Policy and Governance in Healthcare (passage 1)Recommended in: AI Policy and Governance in Healthcare (passage 2)
• CDC, April 2026Cited in: AI Policy and Governance in Healthcare
• CDC, March 2026Cited in: AI Policy and Governance in Healthcare
• Implementing Machine Learning in Health Care — Addressing Ethical ChallengesDanton S. Char; Nigam H. Shah; David Magnus. 2018. New England Journal of Medicinejournal articleCited in: AI Policy and Governance in Healthcare
• Ensuring a National Policy Framework for Artificial IntelligenceCited in: AI Policy and Governance in Healthcare
• EU AI ActCited in: AI Policy and Governance in Healthcare (passage 1); AI Policy and Governance in Healthcare (passage 2)
• European Commission Work Programme 2025Cited in: AI Policy and Governance in Healthcare
• European Commission, 2026Cited in: AI Policy and Governance in Healthcare
• FDA CDS FAQsCited in: AI Policy and Governance in Healthcare
• FDA CDS Guidance, Jan 2026Cited in: AI Policy and Governance in Healthcare (passage 1); AI Policy and Governance in Healthcare (passage 2); AI Policy and Governance in Healthcare (passage 3); AI Policy and Governance in Healthcare (passage 4)
• The Clinician and Dataset Shift in Artificial IntelligenceSamuel G. Finlayson; Adarsh Subbaswamy; Karandeep Singh; et al. 2021. New England Journal of Medicinejournal articleCited in: AI Policy and Governance in Healthcare
• Advancing healthcare AI governance through a comprehensive maturity model based on systematic reviewRowan Hussein; Anna Zink; Bashar Ramadan; et al. 2026. npj Digital Medicinejournal articleCited in: AI Policy and Governance in Healthcare
• IMDRF, 2021: AI/ML-Based Software as Medical DeviceCited in: AI Policy and Governance in Healthcare
• Roles for AI in Implementation of Medicaid Work RequirementsMichelle M. Mello; Himaja Nagireddy. 2026. JAMA Health Forumjournal articleCited in: AI Policy and Governance in Healthcare
• Model CardsCited in: AI Policy and Governance in Healthcare (passage 1)Recommended in: AI Policy and Governance in Healthcare (passage 2)
• NIST AI Risk Management FrameworkCited in: AI Policy and Governance in Healthcare (passage 1); AI Policy and Governance in Healthcare (passage 2)
• NIST, 2023Cited in: AI Policy and Governance in Healthcare
• NIST, 2026Cited in: AI Policy and Governance in Healthcare
• OECD AI PrinciplesCited in: AI Policy and Governance in Healthcare
• official EU AI Act textCited in: AI Policy and Governance in Healthcare (passage 1); AI Policy and Governance in Healthcare (passage 2); AI Policy and Governance in Healthcare (passage 3)
• Operation TrialBlazerCited in: AI Policy and Governance in Healthcare
• Predetermined Change Control Plans for ML-Enabled DevicesCited in: AI Policy and Governance in Healthcare
• Price, 2017, Michigan Law ReviewCited in: AI Policy and Governance in Healthcare (passage 1)Recommended in: AI Policy and Governance in Healthcare (passage 2)
• A governance model for the application of AI in health careSandeep Reddy; Sonia Allan; Simon Coghlan; et al. 2020. Journal of the American Medical Informatics Associationjournal articleCited in: AI Policy and Governance in Healthcare (passage 1)Recommended in: AI Policy and Governance in Healthcare (passage 2)
• Ropes & Gray analysis, 2026Cited in: AI Policy and Governance in Healthcare
• Software and AI as a Medical Device Change ProgrammeCited in: AI Policy and Governance in Healthcare (passage 1)Recommended in: AI Policy and Governance in Healthcare (passage 2)
• Improving Medical Device Regulation: The United States and Europe in PerspectiveCORINNA SORENSON; MICHAEL DRUMMOND. 2014. The Milbank Quarterlyjournal articleCited in: AI Policy and Governance in Healthcare
• Teslo, New York Times, September 2026Cited in: AI Policy and Governance in Healthcare
• UNESCO Recommendation on the Ethics of AICited in: AI Policy and Governance in Healthcare
• WHO Ethics and Governance of AI for HealthCited in: AI Policy and Governance in Healthcare (passage 1); AI Policy and Governance in Healthcare (passage 2); AI Policy and Governance in Healthcare (passage 3)Recommended in: AI Policy and Governance in Healthcare (passage 4)
• WHO, 2024Cited in: AI Policy and Governance in Healthcare (passage 1); AI Policy and Governance in Healthcare (passage 2); AI Policy and Governance in Healthcare (passage 3); AI Policy and Governance in Healthcare (passage 4); AI Policy and Governance in Healthcare (passage 5); AI Policy and Governance in Healthcare (passage 6)
• Health Systems Govern Only the Tip of the AI IcebergErkin Ötleş; Sara G. Murray; Ashley N. Beecy; et al. 2026. NEJM AIjournal articleCited in: AI Policy and Governance in Healthcare

Recommended reading

• Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleRecommended in: AI Policy and Governance in Healthcare
• Benjamens et al., 2020, npj Digital Medicine - The state of AI-based FDA-approved medical devicesRecommended in: AI Policy and Governance in Healthcare
• Char et al., 2018, NEJM - Implementing ML in Health Care: Ethical ChallengesRecommended in: AI Policy and Governance in Healthcare
• EU AI Act: Official TextRecommended in: AI Policy and Governance in Healthcare
• EUDAMEDRecommended in: AI Policy and Governance in Healthcare
• FDA 510(k) DatabaseRecommended in: AI Policy and Governance in Healthcare
• FDA AI-Enabled Medical Device ListRecommended in: AI Policy and Governance in Healthcare
• FDA, 2021 - AI/ML Action PlanRecommended in: AI Policy and Governance in Healthcare
• Finlayson et al., 2021, NEJMRecommended in: AI Policy and Governance in Healthcare
• Gerke et al., 2020, The Need for a System View to Regulate AI/ML-Based Medical DevicesRecommended in: AI Policy and Governance in Healthcare
• Google Model Cards ToolkitRecommended in: AI Policy and Governance in Healthcare
• International Medical Device Regulators Forum (IMDRF)Recommended in: AI Policy and Governance in Healthcare
• Life Sciences AI Handbook section on clinical trial latency in the AI eraRecommended in: AI Policy and Governance in Healthcare
• OCC/Federal Reserve: SR 11-7Recommended in: AI Policy and Governance in Healthcare

AI, Health Misinformation, and the Infodemic

References

• Birds of a feather don’t fact-check each other: Partisanship and the evaluation of news in Twitter’s Birdwatch crowdsourced fact-checking programJennifer Allen; Cameron Martel; David G Rand. 2022. CHI Conference on Human Factors in Computing Systemsconference paperCited in: AI, Health Misinformation, and the Infodemic
• How Structural Racism Works — Racist Policies as a Root Cause of U.S. Racial Health InequitiesZinzi D. Bailey; Justin M. Feldman; Mary T. Bassett. 2021. New England Journal of Medicinejournal articleCited in: AI, Health Misinformation, and the Infodemic
• Good News about Bad News: Gamified Inoculation Boosts Confidence and Cognitive Immunity Against Fake NewsMelisa Basol; Jon Roozenbeek; Sander Van der Linden. 2020. Journal of Cognitionjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Bateman et al., 2024, AJPHCited in: AI, Health Misinformation, and the Infodemic
• Low Health Literacy and Health Outcomes: An Updated Systematic ReviewNancy D. Berkman; Stacey L. Sheridan; Katrina E. Donahue; et al. 2011. Annals of Internal Medicinejournal articleCited in: AI, Health Misinformation, and the Infodemic
• Bernstein, 2021, Washington PostCited in: AI, Health Misinformation, and the Infodemic
• Breakstone et al., 2021Cited in: AI, Health Misinformation, and the Infodemic
• Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine DebateDavid A. Broniatowski; Amelia M. Jamison; SiHua Qi; et al. 2018. American Journal of Public Healthjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Brundage et al., 2020, preprintCited in: AI, Health Misinformation, and the Infodemic
• C2PA, 2023Cited in: AI, Health Misinformation, and the Infodemic
• Congress, 2025Cited in: AI, Health Misinformation, and the Infodemic
• The emerging landscape of performance-enhancing peptides modulating GH-IGF1 axis: bridging the gap between clinical evidence and patient self-administrationAleksander Dominikowski; Zofia Rękoś; Michał Olejarz; et al. 2026. Frontiers in Endocrinologyjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Abstracts written by ChatGPT fool scientistsHolly Else. 2023. Naturejournal articleCited in: AI, Health Misinformation, and the Infodemic
• EU Digital Services Act, 2022Cited in: AI, Health Misinformation, and the Infodemic
• Fernandez et al., 2023Cited in: AI, Health Misinformation, and the Infodemic
• Freimuth et al., 2014Cited in: AI, Health Misinformation, and the Infodemic
• FTC, 2026Cited in: AI, Health Misinformation, and the Infodemic
• Funk & Tyson, 2022, Pew ResearchCited in: AI, Health Misinformation, and the Infodemic
• Under the shadow of Tuskegee: African Americans and health care.V N Gamble. 1997. American Journal of Public Healthjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewersCatherine A. Gao; Frederick M. Howard; Nikolay S. Markov; et al. 2023. npj Digital Medicinejournal articleCited in: AI, Health Misinformation, and the Infodemic (passage 1); AI, Health Misinformation, and the Infodemic (passage 2)
• Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021Cited in: AI, Health Misinformation, and the Infodemic
• Impurity profiling of the most frequently encountered falsified polypeptide drugs on the Belgian marketSteven Janvier; Karlien Cheyns; Michaël Canfyn; et al. 2018. Talantajournal articleCited in: AI, Health Misinformation, and the Infodemic
• Deepfakes: Trick or treat?Jan Kietzmann; Linda W. Lee; Ian P. McCarthy; et al. 2020. Business Horizonsjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Kirchner, 2023, MIT Technology ReviewCited in: AI, Health Misinformation, and the Infodemic
• Kosseff, 2019Cited in: AI, Health Misinformation, and the Infodemic
• Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on TwitterRamez Kouzy; Joseph Abi Jaoude; Afif Kraitem; et al. 2020. Cureusjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Lakoff, 2018Cited in: AI, Health Misinformation, and the Infodemic
• Lewandowsky et al., 2012Cited in: AI, Health Misinformation, and the Infodemic
• Lewandowsky et al., 2020Cited in: AI, Health Misinformation, and the Infodemic
• Li et al., 2018Cited in: AI, Health Misinformation, and the Infodemic
• Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USASahil Loomba; Alexandre de Figueiredo; Simon J. Piatek; et al. 2021. Nature Human Behaviourjournal articleCited in: AI, Health Misinformation, and the Infodemic (passage 1); AI, Health Misinformation, and the Infodemic (passage 2)
• Long-term effectiveness of inoculation against misinformation: Three longitudinal experiments.Rakoen Maertens; Jon Roozenbeek; Melisa Basol; et al. 2021. Journal of Experimental Psychology: Appliedjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Some Contemporary ApproachesWilliam J. McGuire. 1964. Advances in Experimental Social Psychologybook chapterCited in: AI, Health Misinformation, and the Infodemic
• Social and Heuristic Approaches to Credibility Evaluation OnlineMiriam J. Metzger; Andrew J. Flanagin; Ryan B. Medders. 2010. Journal of Communicationjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Montanaro, 2021, NPRCited in: AI, Health Misinformation, and the Infodemic
• Artificial intelligence and increasing misinformationScott Monteith; Tasha Glenn; John R. Geddes; et al. 2024. The British Journal of Psychiatryjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Confirmation Bias: A Ubiquitous Phenomenon in Many GuisesRaymond S. Nickerson. 1998. Review of General Psychologyjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Prior exposure increases perceived accuracy of fake news.Gordon Pennycook; Tyrone D. Cannon; David G. Rand. 2018. Journal of Experimental Psychology: Generaljournal articleCited in: AI, Health Misinformation, and the Infodemic
• Shifting attention to accuracy can reduce misinformation onlineGordon Pennycook; Ziv Epstein; Mohsen Mosleh; et al. 2021. Naturejournal articleCited in: AI, Health Misinformation, and the Infodemic
• The Determinants of Trust and Credibility in Environmental Risk Communication: An Empirical StudyRichard G. Peters; Vincent T. Covello; David B. McCallum. 1997. Risk Analysisjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Online misinformation is linked to early COVID-19 vaccination hesitancy and refusalFrancesco Pierri; Brea L. Perry; Matthew R. DeVerna; et al. 2022. Scientific Reportsjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Beyond Accuracy: A Mixed-Methods Audit of Chain-of-Thought Failures in LLM-Based COVID-19 Vaccine Stance DetectionAndreas Praschk; Valentin Fischill-Neudeck; Thomas Caspari; et al. 2026. Journal of Medical Systemsjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Susceptibility to misinformation about COVID-19 around the worldJon Roozenbeek; Claudia R. Schneider; Sarah Dryhurst; et al. 2020. Royal Society Open Sciencejournal articleCited in: AI, Health Misinformation, and the Infodemic (passage 1); AI, Health Misinformation, and the Infodemic (passage 2)
• Psychological inoculation improves resilience against misinformation on social mediaJon Roozenbeek; Sander van der Linden; Beth Goldberg; et al. 2022. Science Advancesjournal articleCited in: AI, Health Misinformation, and the Infodemic (passage 1); AI, Health Misinformation, and the Infodemic (passage 2); AI, Health Misinformation, and the Infodemic (passage 3)
• Rosen, 2020Cited in: AI, Health Misinformation, and the Infodemic
• Generative AI and health misinformation: production, propagation, and mitigation—a systematic reviewHamid Reza Saeidnia; Shamim Jahani; Nasrin Ghiasi; et al. 2026. BMC Public Healthjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Effect of Hydroxychloroquine on Clinical Status at 14 Days in Hospitalized Patients With COVID-19Wesley H. Self; Matthew W. Semler; Lindsay M. Leither; et al. 2020. JAMAjournal articleCited in: AI, Health Misinformation, and the Infodemic
• Swire-Thompson et al., 2020Cited in: AI, Health Misinformation, and the Infodemic
• Teo, 2021Cited in: AI, Health Misinformation, and the Infodemic
• U.S. Surgeon General, 2021Cited in: AI, Health Misinformation, and the Infodemic
• The spread of true and false news onlineSoroush Vosoughi; Deb Roy; Sinan Aral. 2018. Sciencejournal articleCited in: AI, Health Misinformation, and the Infodemic
• Wardle & Derakhshan, 2017Cited in: AI, Health Misinformation, and the Infodemic
• The Emergence of Deepfake Technology: A ReviewMika Westerlund. 2019. Technology Innovation Management Reviewjournal articleCited in: AI, Health Misinformation, and the Infodemic
• WHO, 2017Cited in: AI, Health Misinformation, and the Infodemic
• WHO, 2020Cited in: AI, Health Misinformation, and the Infodemic
• WHO, 2020Cited in: AI, Health Misinformation, and the Infodemic
• WHO, 2021Cited in: AI, Health Misinformation, and the Infodemic
• Wineburg & McGrew, 2016Cited in: AI, Health Misinformation, and the Infodemic
• YouTube, 2021Cited in: AI, Health Misinformation, and the Infodemic
• How to fight an infodemicJohn Zarocostas. 2020. The Lancetjournal articleCited in: AI, Health Misinformation, and the Infodemic

Large Language Models in Public Health: Theory and Practice

References

• Artificial Hallucinations in ChatGPT: Implications in Scientific WritingHussam Alkaissi; Samy I McFarlane. 2023. Cureusjournal articleCited in: Large Language Models in Public Health: Theory and Practice (passage 1)Recommended in: Large Language Models in Public Health: Theory and Practice (passage 2)
• Claude 2Cited in: Large Language Models in Public Health: Theory and Practice
• Claude 3 familyCited in: Large Language Models in Public Health: Theory and Practice (passage 1); Large Language Models in Public Health: Theory and Practice (passage 2)
• Claude 3.5 SonnetCited in: Large Language Models in Public Health: Theory and Practice
• Claude Haiku 4.5Cited in: Large Language Models in Public Health: Theory and Practice
• Claude Opus 4.1Cited in: Large Language Models in Public Health: Theory and Practice
• Claude Opus 4.5Cited in: Large Language Models in Public Health: Theory and Practice
• Claude Sonnet 4.5Cited in: Large Language Models in Public Health: Theory and Practice
• DeepSeek V3.2-ExpCited in: Large Language Models in Public Health: Theory and Practice (passage 1); Large Language Models in Public Health: Theory and Practice (passage 2)
• Gemini 2.0 FlashCited in: Large Language Models in Public Health: Theory and Practice
• Gemini 2.5 ProCited in: Large Language Models in Public Health: Theory and Practice
• Gemini 2.5 Pro & Flash GACited in: Large Language Models in Public Health: Theory and Practice
• Gemini 3 ProCited in: Large Language Models in Public Health: Theory and Practice
• GPT-4Cited in: Large Language Models in Public Health: Theory and Practice
• GPT-4oCited in: Large Language Models in Public Health: Theory and Practice
• GPT-4VCited in: Large Language Models in Public Health: Theory and Practice
• GPT-5Cited in: Large Language Models in Public Health: Theory and Practice
• GPT-5.1Cited in: Large Language Models in Public Health: Theory and Practice
• GPT-5.1-Codex-MaxCited in: Large Language Models in Public Health: Theory and Practice
• GPT-5.2Cited in: Large Language Models in Public Health: Theory and Practice
• Grok 4Cited in: Large Language Models in Public Health: Theory and Practice
• Grok 4 FastCited in: Large Language Models in Public Health: Theory and Practice
• Grok 4.1Cited in: Large Language Models in Public Health: Theory and Practice
• HHS HIPAA EnforcementCited in: Large Language Models in Public Health: Theory and Practice
• How AI responds to common HIV/AIDS questions: ChatGPT versus DeepSeekHui Huang; Huichao Zhang; Xinxin Qin; et al. 2026. Frontiers in Public Healthjournal articleCited in: Large Language Models in Public Health: Theory and Practice
• Survey of Hallucination in Natural Language GenerationZiwei Ji; Nayeon Lee; Rita Frieske; et al. 2023. ACM Computing Surveysjournal articleCited in: Large Language Models in Public Health: Theory and Practice (passage 1)Recommended in: Large Language Models in Public Health: Theory and Practice (passage 2)
• Personas Shift Clinical Action Thresholds in Large Language ModelsEyal Klang; Alon Gorenstein; Mahmud Omar; et al. 2026preprintCited in: Large Language Models in Public Health: Theory and Practice
• OpenAI o1Cited in: Large Language Models in Public Health: Theory and Practice
• OpenAI o3-miniCited in: Large Language Models in Public Health: Theory and Practice
• OpenAI releases ChatGPTCited in: Large Language Models in Public Health: Theory and Practice
• OpenAI, 2023Cited in: Large Language Models in Public Health: Theory and Practice
• OpenAI, January 2026Cited in: Large Language Models in Public Health: Theory and Practice
• Schmidtova et al., 2025Cited in: Large Language Models in Public Health: Theory and Practice
• the fastest-growing consumer application in historyCited in: Large Language Models in Public Health: Theory and Practice
• Tolmachev, Costa-Gomes, & Sounderajah, 2026, Microsoft ResearchCited in: Large Language Models in Public Health: Theory and Practice
• WHO, 2024Cited in: Large Language Models in Public Health: Theory and Practice (passage 1); Large Language Models in Public Health: Theory and Practice (passage 2)

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• AI strategyRecommended in: Large Language Models in Public Health: Theory and Practice
• Alignment ForumRecommended in: Large Language Models in Public Health: Theory and Practice
• Anthropic APIRecommended in: Large Language Models in Public Health: Theory and Practice
• Anthropic ClaudeRecommended in: Large Language Models in Public Health: Theory and Practice
• APHA AI Working GroupRecommended in: Large Language Models in Public Health: Theory and Practice
• Ayers et al., 2023: Comparing Physician and AI Chatbot ResponsesRecommended in: Large Language Models in Public Health: Theory and Practice
• Azure OpenAI ServiceRecommended in: Large Language Models in Public Health: Theory and Practice
• BioBERTRecommended in: Large Language Models in Public Health: Theory and Practice
• Brown et al., 2020: Language Models are Few-Shot Learners (GPT-3)Recommended in: Large Language Models in Public Health: Theory and Practice
• CDC Clear Communication IndexRecommended in: Large Language Models in Public Health: Theory and Practice
• CDC, March 2026Recommended in: Large Language Models in Public Health: Theory and Practice
• CDC’s Vision for Using Artificial Intelligence in Public HealthRecommended in: Large Language Models in Public Health: Theory and Practice
• Claude Constitutional AIRecommended in: Large Language Models in Public Health: Theory and Practice
• Claude Prompt LibraryRecommended in: Large Language Models in Public Health: Theory and Practice
• Coursera: Generative AI for EveryoneRecommended in: Large Language Models in Public Health: Theory and Practice
• Coursera: Generative AI SpecializationRecommended in: Large Language Models in Public Health: Theory and Practice
• DeepLearning.AI: ChatGPT Prompt Engineering for DevelopersRecommended in: Large Language Models in Public Health: Theory and Practice (passage 1); Large Language Models in Public Health: Theory and Practice (passage 2)
• Fast.ai: Practical Deep Learning for CodersRecommended in: Large Language Models in Public Health: Theory and Practice
• FDA, 2021 - AI/ML Action PlanRecommended in: Large Language Models in Public Health: Theory and Practice
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• LangChainRecommended in: Large Language Models in Public Health: Theory and Practice
• Last Week in AIRecommended in: Large Language Models in Public Health: Theory and Practice
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• NIH AI PolicyRecommended in: Large Language Models in Public Health: Theory and Practice
• Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleRecommended in: Large Language Models in Public Health: Theory and Practice
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• Ouyang et al., 2022: Training language models to follow instructions (InstructGPT)Recommended in: Large Language Models in Public Health: Theory and Practice
• Papers with CodeRecommended in: Large Language Models in Public Health: Theory and Practice
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• Large language models encode clinical knowledgeKaran Singhal; Shekoofeh Azizi; Tao Tu; et al. 2023. Naturejournal articleRecommended in: Large Language Models in Public Health: Theory and Practice
• The BatchRecommended in: Large Language Models in Public Health: Theory and Practice
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• Generative Artificial Intelligence in Health and MedicineThe Learning Health System Series; National Academy of Medicine. 2025Recommended in: Large Language Models in Public Health: Theory and Practice
• Thirunavukarasu et al., 2023: Large language models in medicineRecommended in: Large Language Models in Public Health: Theory and Practice
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• Weidinger et al., 2021: Ethical and social risks of harm from Language ModelsRecommended in: Large Language Models in Public Health: Theory and Practice
• WHO Ethics and Governance of AI for HealthRecommended in: Large Language Models in Public Health: Theory and Practice (passage 1); Large Language Models in Public Health: Theory and Practice (passage 2)
• xAI GrokRecommended in: Large Language Models in Public Health: Theory and Practice

Large Language Model Foundations for Public Health

References

• Abridge uses proprietary AI modelsCited in: Large Language Model Foundations for Public Health
• actual GDPval resultsCited in: Large Language Model Foundations for Public Health
• AnthropicCited in: Large Language Model Foundations for Public Health
• bioRxiv/medRxivCited in: Large Language Model Foundations for Public Health
• CDC PHDS, September 2025Cited in: Large Language Model Foundations for Public Health
• CDC, March 2026Cited in: Large Language Model Foundations for Public Health
• CDC’s Vision for Using Artificial Intelligence in Public HealthCited in: Large Language Model Foundations for Public Health
• ChEMBLCited in: Large Language Model Foundations for Public Health
• Claude for HealthcareCited in: Large Language Model Foundations for Public Health
• Claude for Life SciencesCited in: Large Language Model Foundations for Public Health
• ClinicalTrials.govCited in: Large Language Model Foundations for Public Health
• FDA CDS Guidance, Jan 2026Cited in: Large Language Model Foundations for Public Health
• FDA’s January 2025 draft guidance on AI-enabled device software functionsCited in: Large Language Model Foundations for Public Health
• GitHubCited in: Large Language Model Foundations for Public Health
• GitHub availableCited in: Large Language Model Foundations for Public Health
• GoogleCited in: Large Language Model Foundations for Public Health
• DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learningDaya Guo; Dejian Yang; Haowei Zhang; et al. 2025. Naturejournal articleCited in: Large Language Model Foundations for Public Health
• HealthBench was created by OpenAI researchersCited in: Large Language Model Foundations for Public Health
• Healthcare IT NewsCited in: Large Language Model Foundations for Public Health
• HHS AI Use Case InventoryCited in: Large Language Model Foundations for Public Health
• HHS HIPAA EnforcementCited in: Large Language Model Foundations for Public Health
• HHS HIPAA Privacy RuleCited in: Large Language Model Foundations for Public Health
• JAMIA studyCited in: Large Language Model Foundations for Public Health
• Lambert et al., 2024, preprintCited in: Large Language Model Foundations for Public Health
• Lee et al., 2024Cited in: Large Language Model Foundations for Public Health
• Liu et al., 2024Cited in: Large Language Model Foundations for Public Health
• Tokenising the patient journey: from records to representationsFaisal Mahmood; Eric J Topol. 2026. The Lancetjournal articleCited in: Large Language Model Foundations for Public Health
• Mayo Clinic ProceedingsCited in: Large Language Model Foundations for Public Health
• MedidataCited in: Large Language Model Foundations for Public Health
• Mikolov et al., 2013 on distributed representationsCited in: Large Language Model Foundations for Public Health
• Mok, 2023, Business InsiderCited in: Large Language Model Foundations for Public Health
• NEJM AI, August 2025Cited in: Large Language Model Foundations for Public Health
• OCR Breach PortalCited in: Large Language Model Foundations for Public Health
• Open TargetsCited in: Large Language Model Foundations for Public Health
• OpenAICited in: Large Language Model Foundations for Public Health
• OpenAI for HealthcareCited in: Large Language Model Foundations for Public Health
• OpenAI Startup Fund investorCited in: Large Language Model Foundations for Public Health
• Ouyang et al., 2022: Training language models to follow instructions (InstructGPT)Cited in: Large Language Model Foundations for Public Health
• OwkinCited in: Large Language Model Foundations for Public Health
• PMC analysisCited in: Large Language Model Foundations for Public Health
• preprint studyCited in: Large Language Model Foundations for Public Health
• Sennrich et al., 2016 on neural machine translationCited in: Large Language Model Foundations for Public Health
• Shen et al., 2024 on jailbreakingCited in: Large Language Model Foundations for Public Health
• STAT News coverageCited in: Large Language Model Foundations for Public Health
• Sweeney, 2000 on uniqueness of simple demographicsCited in: Large Language Model Foundations for Public Health
• The Illustrated TransformerCited in: Large Language Model Foundations for Public Health (passage 1)Recommended in: Large Language Model Foundations for Public Health (passage 2)
• Uses OpenAI APIsCited in: Large Language Model Foundations for Public Health
• Vaswani et al., 2017: Attention Is All You NeedCited in: Large Language Model Foundations for Public Health
• The EU General Data Protection Regulation (GDPR)Paul Voigt; Axel von dem Bussche. 2017bookCited in: Large Language Model Foundations for Public Health

Recommended reading

• Anthropic EnterpriseRecommended in: Large Language Model Foundations for Public Health
• AWS HealthScribeRecommended in: Large Language Model Foundations for Public Health
• Azure OpenAI ServiceRecommended in: Large Language Model Foundations for Public Health
• DeepSeek V3.2-ExpRecommended in: Large Language Model Foundations for Public Health
• Google Cloud HealthcareRecommended in: Large Language Model Foundations for Public Health (passage 1); Large Language Model Foundations for Public Health (passage 2)
• Meta LlamaRecommended in: Large Language Model Foundations for Public Health
• Microsoft DAX CopilotRecommended in: Large Language Model Foundations for Public Health
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• OpenAI EnterpriseRecommended in: Large Language Model Foundations for Public Health
• Physician AI Handbook clinical data chapterRecommended in: Large Language Model Foundations for Public Health

Selecting and Using Large Language Models

References

• Claude for HealthcareCited in: Selecting and Using Large Language Models
• Claude Haiku 4.5Cited in: Selecting and Using Large Language Models
• Claude Opus 4.5Cited in: Selecting and Using Large Language Models
• Claude Sonnet 4.5Cited in: Selecting and Using Large Language Models
• Gemini 3 ProCited in: Selecting and Using Large Language Models
• GPT-5.2Cited in: Selecting and Using Large Language Models
• Grok 4.1Cited in: Selecting and Using Large Language Models
• Wei et al., 2022Cited in: Selecting and Using Large Language Models
• WHO, 2024Cited in: Selecting and Using Large Language Models

Recommended reading

• Claude 3 familyRecommended in: Selecting and Using Large Language Models
• DeepSeek V3.2-ExpRecommended in: Selecting and Using Large Language Models
• Google DeepMindRecommended in: Selecting and Using Large Language Models
• LMSys Chatbot ArenaRecommended in: Selecting and Using Large Language Models
• Microsoft CopilotRecommended in: Selecting and Using Large Language Models
• Mistral AIRecommended in: Selecting and Using Large Language Models
• OpenAI APIRecommended in: Selecting and Using Large Language Models
• Perplexity AIRecommended in: Selecting and Using Large Language Models
• xAI GrokRecommended in: Selecting and Using Large Language Models

Emerging AI Architectures for Public Health

References

• Liu et al., 2023, NeurIPS - Visual Instruction TuningCited in: Emerging AI Architectures for Public Health
• RAG in Health Care: A Novel Framework for Improving Communication and Decision-Making by Addressing LLM LimitationsKaren Ka Yan Ng; Izuki Matsuba; Peter Chengming Zhang. 2025. NEJM AIjournal articleCited in: Emerging AI Architectures for Public Health
• Touvron et al., 2023, Meta AI - Llama 2Cited in: Emerging AI Architectures for Public Health
• Yao et al., 2023, ICLR - ReAct: Reasoning and ActingCited in: Emerging AI Architectures for Public Health

Large Language Model Exercises for Public Health

References

• Simple Demographics Often Identify People UniquelySweeney L. 2000. Carnegie Mellon University, Data Privacy Working Paper 3Cited in: Large Language Model Exercises for Public Health

AI-Driven Behavioral Interventions in Public Health

References

• Which Healthy Eating Nudges Work Best? A Meta-Analysis of Field ExperimentsRomain Cadario; Pierre Chandon. 2020. Marketing Sciencejournal articleCited in: AI-Driven Behavioral Interventions in Public Health
• Sycophantic AI decreases prosocial intentions and promotes dependenceMyra Cheng; Cinoo Lee; Pranav Khadpe; et al. 2026. Sciencejournal articleCited in: AI-Driven Behavioral Interventions in Public Health
• Economic impact of medication non-adherence by disease groups: a systematic reviewRachelle Louise Cutler; Fernando Fernandez-Llimos; Michael Frommer; et al. 2018. BMJ Openjournal articleCited in: AI-Driven Behavioral Interventions in Public Health
• Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled TrialKathleen Kara Fitzpatrick; Alison Darcy; Molly Vierhile. 2017. JMIR Mental Healthjournal articleCited in: AI-Driven Behavioral Interventions in Public Health
• Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartStepsPredrag Klasnja; Shawna Smith; Nicholas J Seewald; et al. 2019. Annals of Behavioral Medicinejournal articleCited in: AI-Driven Behavioral Interventions in Public Health (passage 1); AI-Driven Behavioral Interventions in Public Health (passage 2)
• Data mining for health: staking out the ethical territory of digital phenotypingNicole Martinez-Martin; Thomas R. Insel; Paul Dagum; et al. 2018. npj Digital Medicinejournal articleCited in: AI-Driven Behavioral Interventions in Public Health (passage 1)Recommended in: AI-Driven Behavioral Interventions in Public Health (passage 2)
• Prevalence of Mental Health Discussions in Publicly Available Generative AI ConversationsRyan K. McBain. 2026. JAMA Network Openjournal articleCited in: AI-Driven Behavioral Interventions in Public Health
• Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior SupportInbal Nahum-Shani; Shawna N Smith; Bonnie J Spring; et al. 2018. Annals of Behavioral Medicinejournal articleCited in: AI-Driven Behavioral Interventions in Public Health (passage 1); AI-Driven Behavioral Interventions in Public Health (passage 2)Recommended in: AI-Driven Behavioral Interventions in Public Health (passage 3)
• Delivering “Just-In-Time” Smoking Cessation Support Via Mobile Phones: Current Knowledge and Future Directions: Table 1.Felix Naughton. 2016. Nicotine & Tobacco Researchjournal articleCited in: AI-Driven Behavioral Interventions in Public Health (passage 1); AI-Driven Behavioral Interventions in Public Health (passage 2)
• Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory StudySohrab Saeb; Mi Zhang; Christopher J Karr; et al. 2015. Journal of Medical Internet Researchjournal articleCited in: AI-Driven Behavioral Interventions in Public Health
• Systematic review and meta-analysis of the effectiveness of chatbots on lifestyle behavioursBen Singh; Timothy Olds; Jacinta Brinsley; et al. 2023. npj Digital Medicinejournal articleCited in: AI-Driven Behavioral Interventions in Public Health (passage 1)Recommended in: AI-Driven Behavioral Interventions in Public Health (passage 2)
• Thaler & Sunstein, 2008Cited in: AI-Driven Behavioral Interventions in Public Health (passage 1); AI-Driven Behavioral Interventions in Public Health (passage 2)Recommended in: AI-Driven Behavioral Interventions in Public Health (passage 3)
• Torous et al., 2024Cited in: AI-Driven Behavioral Interventions in Public Health (passage 1)Recommended in: AI-Driven Behavioral Interventions in Public Health (passage 2)

Recommended reading

• HeartStepsRecommended in: AI-Driven Behavioral Interventions in Public Health
• Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleRecommended in: AI-Driven Behavioral Interventions in Public Health
• WHO Infodemic ManagementRecommended in: AI-Driven Behavioral Interventions in Public Health
• WoebotRecommended in: AI-Driven Behavioral Interventions in Public Health

Appendices

References

References

• Antimicrobial Selection by a ComputerVictor L. Yu. 1979. JAMAjournal articleCited in: References

Recommended reading

• publichealthaihandbook.comRecommended in: References

Quick Reference: All Chapter Summaries (TL;DRs)

References

• Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adultsMajid Afshar; Felice Resnik; Cara Joyce; et al. 2025. Nature Medicinejournal articleCited in: Quick Reference: All Chapter Summaries (TL;DRs)
• Development and validation of an overdose risk prediction tool using prescription drug monitoring program dataWalid F. Gellad; Qingnan Yang; Kayleigh M. Adamson; et al. 2023. Drug and Alcohol Dependencejournal articleCited in: Quick Reference: All Chapter Summaries (TL;DRs)
• Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published PapersDong Wook Kim; Hye Young Jang; Kyung Won Kim; et al. 2019. Korean Journal of Radiologyjournal articleCited in: Quick Reference: All Chapter Summaries (TL;DRs)
• Wong et al., 2021Cited in: Quick Reference: All Chapter Summaries (TL;DRs)

Glossary

Recommended reading

• Brief Definitions of Key Terms in AIRecommended in: Glossary
• CDC Glossary of Epidemiology TermsRecommended in: Glossary
• Google Machine Learning GlossaryRecommended in: Glossary
• ML.NET Machine Learning GlossaryRecommended in: Glossary
• Software as a Medical Device GuidanceRecommended in: Glossary
• WHO Ethics and Governance of AI for HealthRecommended in: Glossary
• WHO Health TopicsRecommended in: Glossary

Case Study Library - Overview

References

• A clinically applicable approach to continuous prediction of future acute kidney injuryNenad Tomašev; Xavier Glorot; Jack W. Rae; et al. 2019. Naturejournal articleCited in: Case Study Library - Overview
• WHO, 2024Cited in: Case Study Library - Overview

Case Study Library - Full Cases

References

• Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: Case Study Library - Full Cases
• Outcomes of Treatment for Hepatitis C Virus Infection by Primary Care ProvidersSanjeev Arora; Karla Thornton; Glen Murata; et al. 2011. New England Journal of Medicinejournal articleCited in: Case Study Library - Full Cases
• Arora et al., 2011, NEJMCited in: Case Study Library - Full Cases
• Barron et al., 2018, BMJ Global Health - MomConnect ImplementationCited in: Case Study Library - Full Cases
• Bertsimas et al., 2022, Manufacturing & Service Operations ManagementCited in: Case Study Library - Full Cases
• BlueDotCited in: Case Study Library - Full Cases
• Bogoch et al., 2020, Journal of Travel MedicineCited in: Case Study Library - Full Cases
• Brownstein et al., 2008Cited in: Case Study Library - Full Cases
• CDCCited in: Case Study Library - Full Cases
• Chen et al., 2020, Journal of Rural HealthCited in: Case Study Library - Full Cases
• Chouldechova et al., 2018, FATCited in: Case Study Library - Full Cases
• Natural Language Processing of Social Media as Screening for Suicide RiskGlen Coppersmith; Ryan Leary; Patrick Crutchley; et al. 2018. Biomedical Informatics Insightsjournal articleCited in: Case Study Library - Full Cases
• Crisis Text Line, 2020, Impact ReportCited in: Case Study Library - Full Cases
• DeepMind, 2017Cited in: Case Study Library - Full Cases
• Emanuel et al., 2020, NEJMCited in: Case Study Library - Full Cases
• Eubanks, 2018, Automating InequalityCited in: Case Study Library - Full Cases
• FDACited in: Case Study Library - Full Cases
• FDA K211678Cited in: Case Study Library - Full Cases
• FDA, 2023, Discussion PaperCited in: Case Study Library - Full Cases
• Freeman et al., 2021, Lancet Digital HealthCited in: Case Study Library - Full Cases
• Garnacho-Montero & Martín-Loeches, 2020, Intensive Care MedicineCited in: Case Study Library - Full Cases
• Detecting influenza epidemics using search engine query dataJeremy Ginsberg; Matthew H. Mohebbi; Rajan S. Patel; et al. 2009. Naturejournal articleCited in: Case Study Library - Full Cases
• Gliatto & Rai, 1999, American Family PhysicianCited in: Case Study Library - Full Cases
• Guidelines for reinforcement learning in healthcareOmer Gottesman; Fredrik Johansson; Matthieu Komorowski; et al. 2019. Nature Medicinejournal articleCited in: Case Study Library - Full Cases
• Hypertext, 2025 - Google AI IntegrationCited in: Case Study Library - Full Cases
• IDinsight, 2024Cited in: Case Study Library - Full Cases
• Jumper et al., 2021, NatureCited in: Case Study Library - Full Cases
• KC & Terwiesch, 2012, M&SOMCited in: Case Study Library - Full Cases
• Knight et al., 2020, BMJCited in: Case Study Library - Full Cases
• Komorowski et al., 2018, Nature MedicineCited in: Case Study Library - Full Cases
• The Parable of Google Flu: Traps in Big Data AnalysisDavid Lazer; Ryan Kennedy; Gary King; et al. 2014. Sciencejournal articleCited in: Case Study Library - Full Cases
• Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy studyKristina Lång; Viktoria Josefsson; Anna-Maria Larsson; et al. 2023. The Lancet Oncologyjournal articleCited in: Case Study Library - Full Cases
• Mak & Pichika, 2019, Drug Discovery TodayCited in: Case Study Library - Full Cases
• McKinney et al., 2020, NatureCited in: Case Study Library - Full Cases
• Mehrotra et al., 2020, Health AffairsCited in: Case Study Library - Full Cases
• NCT04526106Cited in: Case Study Library - Full Cases
• NCT04737304Cited in: Case Study Library - Full Cases
• NCT05975983Cited in: Case Study Library - Full Cases
• Powles & Hodson, 2017, Health and TechnologyCited in: Case Study Library - Full Cases
• Public Health England, 2020Cited in: Case Study Library - Full Cases
• Ravi, 2020, LancetCited in: Case Study Library - Full Cases
• Razai et al., 2021, BMJCited in: Case Study Library - Full Cases
• Reach Digital Health, 2024 - MomConnect 10-Year MilestoneCited in: Case Study Library - Full Cases
• Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scansMichael Roberts; Derek Driggs; Matthew Thorpe; et al. 2021. Nature Machine Intelligencejournal articleCited in: Case Study Library - Full Cases
• Salim et al., 2020, JAMA OncologyCited in: Case Study Library - Full Cases
• Savulescu et al., 2020, BMJCited in: Case Study Library - Full Cases
• Schmidt et al., 2020, NEJMCited in: Case Study Library - Full Cases
• Schneider et al., 2019, Nature Reviews Drug DiscoveryCited in: Case Study Library - Full Cases
• Schäfer et al., 2023, Health Care Management ScienceCited in: Case Study Library - Full Cases
• Sumitomo Pharma, 2020Cited in: Case Study Library - Full Cases
• Sumitomo Pharma, 2023Cited in: Case Study Library - Full Cases
• A clinically applicable approach to continuous prediction of future acute kidney injuryNenad Tomašev; Xavier Glorot; Jack W. Rae; et al. 2019. Naturejournal articleCited in: Case Study Library - Full Cases
• UK Information Commissioner’s Office, 2017Cited in: Case Study Library - Full Cases
• Vaithianathan et al., 2017Cited in: Case Study Library - Full Cases
• Vaithianathan et al., 2017Cited in: Case Study Library - Full Cases
• WHO, 2024Cited in: Case Study Library - Full Cases
• Wynants et al., 2020, BMJCited in: Case Study Library - Full Cases
• Yu and Madoff, 2004Cited in: Case Study Library - Full Cases

The AI Morgue: Failure Post-Mortems

References

• A Path for Translation of Machine Learning Products into Healthcare Delivery2020. EMJ Innovationsjournal articleCited in: The AI Morgue: Failure Post-Mortems
• COVID-19 Contact Tracing and Data Protection Can Go TogetherJohannes Abeler; Matthias Bäcker; Ulf Buermeyer; et al. 2020. JMIR mHealth and uHealthjournal articleCited in: The AI Morgue: Failure Post-Mortems
• Correction: Evaluation of molecular inversion probe versus TruSeq® custom methods for targeted next-generation sequencingRowida Almomani; Margherita Marchi; Maurice Sopacua; et al. 2021. PLOS ONEjournal articleCited in: The AI Morgue: Failure Post-Mortems
• BBC NewsCited in: The AI Morgue: Failure Post-Mortems
• BBC NewsCited in: The AI Morgue: Failure Post-Mortems
• A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic RetinopathyEmma Beede; Elizabeth Baylor; Fred Hersch; et al. 2020. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systemsconference paperCited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2); The AI Morgue: Failure Post-Mortems (passage 3)
• COVID-19 contact tracing: data protection expectations on app developmentCited in: The AI Morgue: Failure Post-Mortems
• DeepMind, 2017Cited in: The AI Morgue: Failure Post-Mortems
• DOI: 10.1056/NEJMms2004740Cited in: The AI Morgue: Failure Post-Mortems
• DOI: 10.7326/M18-1990Cited in: The AI Morgue: Failure Post-Mortems
• Financial TimesCited in: The AI Morgue: Failure Post-Mortems
• Fraser et al., 2018Cited in: The AI Morgue: Failure Post-Mortems
• Clinician Perception of a Machine Learning–Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock*Jennifer C. Ginestra; Heather M. Giannini; William D. Schweickert; et al. 2019. Critical Care Medicinejournal articleCited in: The AI Morgue: Failure Post-Mortems
• Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)Jennifer C. Goldsack; Andrea Coravos; Jessie P. Bakker; et al. 2020. npj Digital Medicinejournal articleCited in: The AI Morgue: Failure Post-Mortems
• Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus PhotographsVarun Gulshan; Lily Peng; Marc Coram; et al. 2016. JAMAjournal articleCited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2)
• Heaven, MIT Technology Review, 2020Cited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2); The AI Morgue: Failure Post-Mortems (passage 3)
• Hern, 2017Cited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2)
• Hernandez & Greenwald, 2018Cited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2)
• Herper, Forbes, February 2017Cited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2)
• Hodson, 2016Cited in: The AI Morgue: Failure Post-Mortems
• With an eye to AI and autonomous diagnosisPearse A. Keane; Eric J. Topol. 2018. npj Digital Medicinejournal articleCited in: The AI Morgue: Failure Post-Mortems
• Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic RetinopathyJonathan Krause; Varun Gulshan; Ehsan Rahimy; et al. 2018. Ophthalmologyjournal articleCited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2)
• Lohr, 2022Cited in: The AI Morgue: Failure Post-Mortems
• Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor unitsAndrea McCoy; Ritankar Das. 2017. BMJ Open Qualityjournal articleCited in: The AI Morgue: Failure Post-Mortems
• NAO correspondenceCited in: The AI Morgue: Failure Post-Mortems
• NatureCited in: The AI Morgue: Failure Post-Mortems
• New ScientistCited in: The AI Morgue: Failure Post-Mortems
• New York TimesCited in: The AI Morgue: Failure Post-Mortems
• Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2); The AI Morgue: Failure Post-Mortems (passage 3)
• Addressing Bias in Artificial Intelligence in Health CareRavi B. Parikh; Stephanie Teeple; Amol S. Navathe. 2019. JAMAjournal articleCited in: The AI Morgue: Failure Post-Mortems
• Large-Scale Assessment of a Smartwatch to Identify Atrial FibrillationMarco V. Perez; Kenneth W. Mahaffey; Haley Hedlin; et al. 2019. New England Journal of Medicinejournal articleCited in: The AI Morgue: Failure Post-Mortems
• Google DeepMind and healthcare in an age of algorithmsJulia Powles; Hal Hodson. 2017. Health and Technologyjournal articleCited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2)
• Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scansMichael Roberts; Derek Driggs; Matthew Thorpe; et al. 2021. Nature Machine Intelligencejournal articleCited in: The AI Morgue: Failure Post-Mortems
• Ross & Swetlitz, 2017Cited in: The AI Morgue: Failure Post-Mortems
• Ross & Swetlitz, 2018Cited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2)
• STAT NewsCited in: The AI Morgue: Failure Post-Mortems
• Straits TimesCited in: The AI Morgue: Failure Post-Mortems
• Strickland, 2019Cited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2)
• Test and Trace in England - progress updateCited in: The AI Morgue: Failure Post-Mortems
• The Cancer LetterCited in: The AI Morgue: Failure Post-Mortems
• The GuardianCited in: The AI Morgue: Failure Post-Mortems
• The GuardianCited in: The AI Morgue: Failure Post-Mortems
• The New YorkerCited in: The AI Morgue: Failure Post-Mortems
• UK Information Commissioner’s Office, 2017Cited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2); The AI Morgue: Failure Post-Mortems (passage 3)
• UK Parliament, 2020Cited in: The AI Morgue: Failure Post-Mortems
• Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive dataMichael Veale; Reuben Binns. 2017. Big Data & Societyjournal articleCited in: The AI Morgue: Failure Post-Mortems
• Washington PostCited in: The AI Morgue: Failure Post-Mortems
• Public attitudes towards COVID‐19 contact tracing apps: A UK‐based focus group studySimon N. Williams; Christopher J. Armitage; Tova Tampe; et al. 2021. Health Expectationsjournal articleCited in: The AI Morgue: Failure Post-Mortems
• Wong et al., 2021Cited in: The AI Morgue: Failure Post-Mortems (passage 1); The AI Morgue: Failure Post-Mortems (passage 2); The AI Morgue: Failure Post-Mortems (passage 3)
• Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisalLaure Wynants; Ben Van Calster; Gary S Collins; et al. 2020. BMJjournal articleCited in: The AI Morgue: Failure Post-Mortems

AI Vendor Evaluation Checklist

References

• A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic RetinopathyEmma Beede; Elizabeth Baylor; Fred Hersch; et al. 2020. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systemsconference paperCited in: AI Vendor Evaluation Checklist
• Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus PhotographsVarun Gulshan; Lily Peng; Marc Coram; et al. 2016. JAMAjournal articleCited in: AI Vendor Evaluation Checklist
• ONC, HTI-1 Final RuleCited in: AI Vendor Evaluation Checklist
• Wong et al., 2021Cited in: AI Vendor Evaluation Checklist

Recommended reading

• FDA, 2021 - AI/ML Action PlanRecommended in: AI Vendor Evaluation Checklist
• https://psnet.ahrq.gov/Recommended in: AI Vendor Evaluation Checklist
• WHO Ethics and Governance of AI for HealthRecommended in: AI Vendor Evaluation Checklist

AI Governance Policy Template

References

• California Legislature, 2024Cited in: AI Governance Policy Template
• Deep Learning bookCited in: AI Governance Policy Template
• DOI: 10.7326/M18-1990Cited in: AI Governance Policy Template
• Executive Order 14179Cited in: AI Governance Policy Template
• FDACited in: AI Governance Policy Template
• FDA, 2021 - AI/ML Action PlanCited in: AI Governance Policy Template
• FDA, 2024Cited in: AI Governance Policy Template
• The Clinician and Dataset Shift in Artificial IntelligenceSamuel G. Finlayson; Adarsh Subbaswamy; Karandeep Singh; et al. 2021. New England Journal of Medicinejournal articleCited in: AI Governance Policy Template
• Goodwin Law analysisCited in: AI Governance Policy Template
• HHSCited in: AI Governance Policy Template
• HHS, 2024Cited in: AI Governance Policy Template (passage 1); AI Governance Policy Template (passage 2)
• Legal analysts expect court challengesCited in: AI Governance Policy Template
• Model Cards for Model ReportingMargaret Mitchell; Simone Wu; Andrew Zaldivar; et al. 2019. Proceedings of the Conference on Fairness, Accountability, and Transparencyconference paperCited in: AI Governance Policy Template (passage 1)Recommended in: AI Governance Policy Template (passage 2)
• Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studiesMyura Nagendran; Yang Chen; Christopher A Lovejoy; et al. 2020. BMJjournal articleCited in: AI Governance Policy Template
• NYCCited in: AI Governance Policy Template
• Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: AI Governance Policy Template (passage 1); AI Governance Policy Template (passage 2)
• official EU AI Act textCited in: AI Governance Policy Template
• publichealthaihandbook.comCited in: AI Governance Policy Template
• Ross & Swetlitz, 2018Cited in: AI Governance Policy Template
• Russell & Norvig, 2020Cited in: AI Governance Policy Template
• White House, 2025Cited in: AI Governance Policy Template
• WHO Ethics and Governance of AI for HealthCited in: AI Governance Policy Template

Career Guide: Pathways in Public Health AI

References

• AMIACited in: Career Guide: Pathways in Public Health AI
• BLSCited in: Career Guide: Pathways in Public Health AI
• BLSCited in: Career Guide: Pathways in Public Health AI
• CDCCited in: Career Guide: Pathways in Public Health AI
• FDA, 2021 - AI/ML Action PlanCited in: Career Guide: Pathways in Public Health AI
• Grand View ResearchCited in: Career Guide: Pathways in Public Health AI
• Applications of digital technology in COVID-19 pandemic planning and responseSera Whitelaw; Mamas A Mamas; Eric Topol; et al. 2020. The Lancet Digital Healthjournal articleCited in: Career Guide: Pathways in Public Health AI

Recommended reading

• @AndrewLBeamRecommended in: Career Guide: Pathways in Public Health AI
• @AndrewYNgRecommended in: Career Guide: Pathways in Public Health AI
• @EricTopolRecommended in: Career Guide: Pathways in Public Health AI
• @oziadiasRecommended in: Career Guide: Pathways in Public Health AI
• An Introduction to Statistical LearningRecommended in: Career Guide: Pathways in Public Health AI
• Artificial Intelligence in MedicineRecommended in: Career Guide: Pathways in Public Health AI
• Behavioral Risk Factor Surveillance System (BRFSS)Recommended in: Career Guide: Pathways in Public Health AI
• CDC WONDERRecommended in: Career Guide: Pathways in Public Health AI
• Cervical Cancer ScreeningRecommended in: Career Guide: Pathways in Public Health AI
• Clinical Decision Support SystemsRecommended in: Career Guide: Pathways in Public Health AI
• CMS Medicare ClaimsRecommended in: Career Guide: Pathways in Public Health AI
• Deep Learning bookRecommended in: Career Guide: Pathways in Public Health AI
• Diabetic Retinopathy DetectionRecommended in: Career Guide: Pathways in Public Health AI
• eICURecommended in: Career Guide: Pathways in Public Health AI
• Fast.ai forumsRecommended in: Career Guide: Pathways in Public Health AI
• Global Health Data Exchange (GHDx)Recommended in: Career Guide: Pathways in Public Health AI
• Gordis EpidemiologyRecommended in: Career Guide: Pathways in Public Health AI
• Hands-On Machine LearningRecommended in: Career Guide: Pathways in Public Health AI
• Heritage Health PrizeRecommended in: Career Guide: Pathways in Public Health AI
• JAMARecommended in: Career Guide: Pathways in Public Health AI
• JMIR Medical InformaticsRecommended in: Career Guide: Pathways in Public Health AI
• Journal of the American Medical Informatics Association (JAMIA)Recommended in: Career Guide: Pathways in Public Health AI
• MIMIC-IIIRecommended in: Career Guide: Pathways in Public Health AI
• ML CollectiveRecommended in: Career Guide: Pathways in Public Health AI
• National Health and Nutrition Examination Survey (NHANES)Recommended in: Career Guide: Pathways in Public Health AI
• Nature Machine IntelligenceRecommended in: Career Guide: Pathways in Public Health AI
• NEJMRecommended in: Career Guide: Pathways in Public Health AI
• npj Digital MedicineRecommended in: Career Guide: Pathways in Public Health AI
• Our World in DataRecommended in: Career Guide: Pathways in Public Health AI
• PLOS Digital HealthRecommended in: Career Guide: Pathways in Public Health AI
• Proceedings of Machine Learning Research (PMLR) - MLHCRecommended in: Career Guide: Pathways in Public Health AI
• r/datascienceRecommended in: Career Guide: Pathways in Public Health AI
• r/healthITRecommended in: Career Guide: Pathways in Public Health AI
• r/MachineLearningRecommended in: Career Guide: Pathways in Public Health AI
• Secondary Analysis of Electronic Health RecordsRecommended in: Career Guide: Pathways in Public Health AI
• The AI Revolution in MedicineRecommended in: Career Guide: Pathways in Public Health AI
• The LancetRecommended in: Career Guide: Pathways in Public Health AI
• The Lancet Digital HealthRecommended in: Career Guide: Pathways in Public Health AI
• Weapons of Math DestructionRecommended in: Career Guide: Pathways in Public Health AI

Frequently Asked Questions

References

• WHO, 2024Cited in: Frequently Asked Questions

Further Reading

References

• The Missing Dimension in Clinical AI: Making Hidden Values VisibleCarey Goldberg; Ran D. Balicer; Mamatha Bhat; et al. 2026. NEJM AIjournal articleCited in: Further Reading

Recommended reading

• 2026 AI for Public Health trackRecommended in: Further Reading
• Harvard BioethicsRecommended in: Further Reading
• safe.aiRecommended in: Further Reading
• Statement on AI RiskRecommended in: Further Reading
• Vital Strategies, February 2026Recommended in: Further Reading

Course Syllabus Template

References

• DOI: 10.5281/zenodo.18263442Cited in: Course Syllabus Template

Recommended reading

• Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.Recommended in: Course Syllabus Template
• Artificial Intelligence and the Implementation ChallengeRecommended in: Course Syllabus Template
• Big Data in Public Health: Terminology, Machine Learning, and PrivacyRecommended in: Course Syllabus Template
• Do no harm: A roadmap for responsible machine learning for health careRecommended in: Course Syllabus Template
• Machine Learning in MedicineRecommended in: Course Syllabus Template
• WHO Ethics and Governance of AI for HealthRecommended in: Course Syllabus Template