References

How to Cite This Handbook

The Public Health AI Handbook is a living, open-access resource that is regularly updated. This page provides guidance on how to properly cite the handbook in your work.

Quick Reference

TipCitation Recommendations
  • Citing the whole handbook? Use the BibTeX entry or format examples below
  • Citing a specific chapter? Use the chapter-level citation format
  • Referencing a study discussed in the handbook? Cite the original source directly
  • Always include: Version number (e.g., Version 1.1)

Citing the Entire Handbook

BibTeX Format

@book{tegomoh2025publichealth,
  title={The Public Health AI Handbook},
  author={Tegomoh, Bryan},
  year={2025},
  version={1.1},
  url={https://publichealthaihandbook.com},
  note={Licensed under CC BY 4.0}
}

APA Format (7th Edition)

Tegomoh, B. (2025). The Public Health AI Handbook: A Practical Guide to
    Artificial Intelligence in Public Health Practice (Version 1.1)
    [Open-access digital handbook]. https://publichealthaihandbook.com

MLA Format (9th Edition)

Tegomoh, Bryan. The Public Health AI Handbook: A Practical Guide to
    Artificial Intelligence in Public Health Practice. Version 1.1,
    2025, https://publichealthaihandbook.com.

Chicago Style (17th Edition)

Tegomoh, Bryan. The Public Health AI Handbook: A Practical Guide to
    Artificial Intelligence in Public Health Practice. Version 1.1.
    2025. https://publichealthaihandbook.com.

Citing a Specific Chapter or Section

When referencing specific content, cite at the chapter level for precision and to credit contributors.

BibTeX Format

@incollection{tegomoh2025history,
  author = {Tegomoh, Bryan},
  title = {AI in Context: A Brief History},
  booktitle = {The Public Health AI Handbook},
  year = {2025},
  version = {1.1},
  url = {https://publichealthaihandbook.com/part1-foundations/chapter01-history.html},
  note = {Chapter 1}
}

APA Format

Tegomoh, B. (2025). AI in Context: A Brief History. In The Public Health AI
    Handbook (Version 1.1). https://publichealthaihandbook.com/part1-foundations/chapter01-history.html

MLA Format

Tegomoh, Bryan. "AI in Context: A Brief History." The Public Health AI Handbook,
    Version 1.1, 2025, https://publichealthaihandbook.com/part1-foundations/chapter01-history.html.

Chicago Style

Tegomoh, Bryan. "AI in Context: A Brief History." In The Public Health AI Handbook,
    Version 1.1. 2025. https://publichealthaihandbook.com/part1-foundations/chapter01-history.html.

Citing Primary Sources Referenced in the Handbook

ImportantBest Practice: Cite the Original Source

When the handbook discusses or summarizes research from other sources (journal articles, reports, datasets, etc.), always cite the original source rather than citing the handbook as a secondary reference.

Why? This: - Gives proper credit to the original researchers - Provides readers with the primary source for verification - Maintains academic integrity and rigor - Follows scholarly citation standards

Example

Incorrect: > According to Tegomoh (2025), MYCIN was an expert system that performed > as well as infectious disease specialists.

Correct: > MYCIN, an expert system for diagnosing bacterial infections, performed > as well as infectious disease specialists in controlled evaluations > (Shortliffe et al., 1975).

How to find original sources: Each chapter includes a “Further Reading” section with full citations of key papers and resources discussed in that chapter.

Versioning and Access Dates

Because this is a living online resource that is updated regularly:

Current Version

  • Version: 1.1
  • Publication Date: 2025
  • What’s New in 1.1: TL;DR summaries at the start of every chapter, real examples like Google Flu Trends and Epic’s sepsis model, practical focus on what actually works and common mistakes to avoid

About Version Numbers

This handbook uses version numbers (e.g., Version 1.1) to track editions. The version number serves as a stable reference point, so access dates are not required when citing versioned content.

License Information

This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

  • You are free to: Share, copy, redistribute, adapt, remix, and build upon this material for any purpose, including commercially, with attribution.
  • For collaboration opportunities: Contact Bryan

Full license details: LICENSE | CC BY 4.0 Legal Code

Need help with citations? Use tools like ZoteroBib or Citation Machine, or email me with questions.

References

Abràmoff, Michael D., Philip T. Lavin, Michele Birch, Nilay Shah, and James C. Folk. 2018. “Pivotal Trial of an Autonomous AI-Based Diagnostic System for Detection of Diabetic Retinopathy in Primary Care Offices.” Npj Digital Medicine 1: 39. https://doi.org/10.1038/s41746-018-0040-6.
Adiasa, Ozi. n.d. “Ozi Adiasa (@Oziadias) on x/Twitter.” Twitter/X profile. https://x.com/oziadias.
American Medical Informatics Association. 2024. “Informatics Workforce Development.” https://amia.org/.
Antoniadi, Anna Markella, Yuhan Du, Yasmine Guendouz, Lijing Wei, Claudia Mazo, Brett A Becker, and Catherine Mooney. 2021. “Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review.” Applied Sciences 11 (11): 5088. https://doi.org/10.3390/app11115088.
Bailey, Zinzi D, Justin M Feldman, and Mary T Lewis. 2017. “Structural Racism and Health Inequities in the USA: Evidence and Interventions.” The Lancet 389 (10077): 1453–63. https://doi.org/10.1016/S0140-6736(17)30569-X.
Barnett, G. Octo, James J. Cimino, Jon A. Hupp, and Edward P. Hoffer. 1987. “DXplain: An Evolving Diagnostic Decision-Support System.” JAMA 258 (1): 67–74. https://doi.org/10.1001/jama.258.1.67.
Beam, Andrew L. n.d. “Andrew Beam (@AndrewLBeam) on x/Twitter.” Twitter/X profile. https://x.com/AndrewLBeam.
Beam, Andrew L, and Isaac S Kohane. 2018. “Big Data and Machine Learning in Health Care.” JAMA 319 (13): 1317–18. https://doi.org/10.1001/jama.2017.18391.
Beede, Emma, Elizabeth Baylor, Fred Hersch, Anna Iurchenko, Lauren Wilcox, Paisan Ruamviboonsuk, and Laura M Vardoulakis. 2020. “A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy,” 1–12. https://doi.org/10.1145/3313831.3376718.
Benjamens, Stan, Pranavsingh Dhunnoo, and Bertalan Meskó. 2020. “The State of Artificial Intelligence-Based FDA-Approved Medical Devices and Algorithms: An Online Database.” Npj Digital Medicine 3 (1): 118. https://doi.org/10.1038/s41746-020-0262-2.
Berkman, Nancy D, Stacey L Sheridan, Katrina E Donahue, David J Halpern, and Karen Crotty. 2011. “Low Health Literacy and Health Outcomes: An Updated Systematic Review.” Annals of Internal Medicine 155 (2): 97–107. https://doi.org/10.7326/0003-4819-155-2-201107190-00005.
Breakstone, Joel, Mark Smith, Sam Wineburg, Amie Rapaport, Jill Carle, Moriah Garland, and Anna Saavedra. 2021. “Lateral Reading: College Students Learn to Critically Evaluate Internet Sources in an Online Course.” Harvard Kennedy School Misinformation Review 2 (1). https://doi.org/10.37016/mr-2020-56.
Brownstein, John S, Clark C Freifeld, Ben Y Reis, and Kenneth D Mandl. 2008. “Surveillance Sans Frontières: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project.” PLoS Medicine 5 (7): e151. https://doi.org/10.1371/journal.pmed.0050151.
Bureau of Labor Statistics, U.S. Department of Labor. 2023a. “Occupational Outlook Handbook: Data Scientists.” U.S. Bureau of Labor Statistics. https://www.bls.gov/ooh/math/data-scientists.htm.
———. 2023b. “Occupational Outlook Handbook: Health Information Technologists and Medical Registrars.” U.S. Bureau of Labor Statistics. https://www.bls.gov/ooh/healthcare/health-information-technologists-and-medical-registrars.htm.
Centers for Disease Control and Prevention. 2023. “Data Modernization Initiative.” https://www.cdc.gov/surveillance/data-modernization/index.html.
Corbett, Kizzmekia S, Barbara Flynn, Katelyn E Foulds, Joseph R Francica, Seyhan Boyoglu-Barnum, Anne P Werner, et al. 2020. “SARS-CoV-2 mRNA Vaccine Design Enabled by Prototype Pathogen Preparedness.” Nature 586 (7830): 567–71. https://doi.org/10.1038/s41586-020-2622-0.
Covello, Vincent T. 2006. “Risk Communication and Message Mapping: A New Tool for Communicating Effectively in Public Health Emergencies and Disasters.” Journal of Emergency Management 4 (3): 25–40.
Cramer, Estee Y, Evan L Ray, Velma K Lopez, Johannes Bracher, Andrea Brennen, Alvaro J Castro Rivadeneira, et al. 2022. “Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the United States.” Proceedings of the National Academy of Sciences 119 (15): e2113561119. https://doi.org/10.1073/pnas.2113561119.
Crevier, Daniel. 1993. AI: The Tumultuous History of the Search for Artificial Intelligence. New York: Basic Books.
Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1:4171–86. https://doi.org/10.18653/v1/N19-1423.
Esteva, Andre, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, and Sebastian Thrun. 2017. “Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks.” Nature 542 (7639): 115–18. https://doi.org/10.1038/nature21056.
European Parliament and Council. 2024. “Regulation (EU) 2024/1689 of the European Parliament and of the Council on Laying down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act).” Official Journal of the European Union. https://artificialintelligenceact.eu/.
European Union. 2022. “Regulation (EU) 2022/2065 on a Single Market for Digital Services (Digital Services Act).” https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32022R2065.
Freifeld, Clark C, Kenneth D Mandl, Ben Y Reis, and John S Brownstein. 2008. “HealthMap: Global Infectious Disease Monitoring Through Automated Classification and Visualization of Internet Media Reports.” Journal of the American Medical Informatics Association 15 (2): 150–57. https://doi.org/10.1197/jamia.M2544.
Freimuth, Vicki S, Donald Musa, Karen Hilyard, Sandra Crouse Quinn, and Keri Kim. 2014. “Trust During the Early Stages of the 2009 H1N1 Pandemic.” Journal of Health Communication 19 (3): 321–39. https://doi.org/10.1080/10810730.2013.811323.
Fry, Erika. 2018. “IBM’s Watson Was Supposed to Transform Health Care. What Happened?” Fortune. https://fortune.com/longform/ibm-watson-health-transformation-flop/.
Fuller, Aidan, Zhong Fan, Charles Day, and Chris Barlow. 2020. “Digital Twin: Enabling Technologies, Challenges and Open Research.” IEEE Access 8: 108952–71. https://doi.org/10.1109/ACCESS.2020.2998358.
Gao, Catherine A, Frederick M Howard, Nina S Markov, Emma C Dyer, Sanjay Ramesh, Yuan Luo, and Alexander T Pearson. 2023. “Comparing Scientific Abstracts Generated by ChatGPT to Real Abstracts with Detectors and Blinded Human Reviewers.” Npj Digital Medicine 6 (1): 75. https://doi.org/10.1038/s41746-023-00819-6.
Ginsberg, Jeremy, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, and Larry Brilliant. 2009. “Detecting Influenza Epidemics Using Search Engine Query Data.” Nature 457 (7232): 1012–14. https://doi.org/10.1038/nature07634.
Grand View Research. 2023. “Artificial Intelligence in Healthcare Market Size, Share & Trends Analysis Report.” https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-healthcare-market.
Gulshan, Varun, Lily Peng, Marc Coram, Martin C. Stumpe, Derek Wu, Arunachalam Narayanaswamy, et al. 2016. “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.” JAMA 316 (22): 2402–10. https://doi.org/10.1001/jama.2016.17216.
Haug, Peter J., and Erica L. Drazen. 2023. “Medical Informatics: Past, Present, Future.” International Journal of Medical Informatics 84 (10): 726–32. https://doi.org/10.1016/j.ijmedinf.2015.07.001.
Hern, Alex. 2017. “Royal Free Breached UK Data Law in 1.6m Patient Deal with Google’s DeepMind.” The Guardian. https://www.theguardian.com/technology/2017/jul/03/google-deepmind-16m-patient-royal-free-deal-data-protection-act.
Hernán, Miguel A, and James M Robins. 2020. Causal Inference: What If. Chapman & Hall/CRC. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/.
Hernandez, Daniela, and Ted Greenwald. 2018. “IBM Pitched Watson as a Revolution in Cancer Care. It’s Nowhere Close.” The Wall Street Journal. https://www.wsj.com/articles/ibm-pitched-watson-as-a-revolution-in-cancer-care-its-nowhere-close-1533961147.
Hodson, Hal. 2017. “DeepMind and Google: The Battle to Control Artificial Intelligence.” The Economist. https://www.economist.com/1843/2017/03/15/deepmind-and-google-the-battle-to-control-artificial-intelligence.
Hutwagner, Lori, William Thompson, G Matthew Seeman, and Tracee Treadwell. 2003. “The Bioterrorism Preparedness and Response Early Aberration Reporting System (EARS).” Journal of Urban Health 80 (Suppl 1): i89–96. https://doi.org/10.1007/PL00022319.
Information Commissioner’s Office. 2017. “Royal Free - Google DeepMind Trial Failed to Comply with Data Protection Law.” https://ico.org.uk/about-the-ico/media-centre/news-and-blogs/2017/07/royal-free-google-deepmind-trial-failed-to-comply-with-data-protection-law/.
Ioannidis, John PA. 2021. “Infection Fatality Rate of COVID-19 Inferred from Seroprevalence Data.” Bulletin of the World Health Organization 99 (1): 19–33F. https://doi.org/10.2471/BLT.20.265892.
Jain, Aarushi, and Nikita Verma. 2022. “India’s New IT Rules and Platform Governance.” Internet Policy Review 11 (1): 1–20. https://doi.org/10.14763/2022.1.1631.
Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, et al. 2021. “Highly Accurate Protein Structure Prediction with AlphaFold.” Nature 596 (7873): 583–89. https://doi.org/10.1038/s41586-021-03819-2.
Kahn, Michael G, Tiffany J Callahan, Juliana Barnard, Alan E Bauck, Jeff Brown, Bruce N Davidson, et al. 2016. “A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data.” eGEMs 4 (1): 1244. https://doi.org/10.13063/2327-9214.1244.
Kaufman, Shachar, Saharon Rosset, and Claudia Perlich. 2012. “Leakage in Data Mining: Formulation, Detection, and Avoidance.” ACM Transactions on Knowledge Discovery from Data (TKDD) 6 (4): 1–21. https://doi.org/10.1145/2382577.2382579.
Kosseff, Jeff. 2019. “The Twenty-Six Words That Created the Internet.” https://doi.org/10.7591/9781501714412.
Krause, Jonathan, Varun Gulshan, Ehsan Rahimy, Peter Karth, Kasumi Widner, Greg S Corrado, Lily Peng, and Dale R Webster. 2018. “Grading of Diabetic Retinopathy Severity from Retinal Fundus Images Using Deep Learning and Large-Scale Clinical Data.” Ophthalmology 125 (8): 1237–44. https://doi.org/10.1016/j.ophtha.2018.02.032.
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” In Advances in Neural Information Processing Systems, 25:1097–1105.
Lakoff, George. 2018. “The Truth Sandwich.” https://georgelakoff.com/2018/10/26/the-truth-sandwich/.
Lazer, David, Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. “The Parable of Google Flu: Traps in Big Data Analysis.” Science 343 (6176): 1203–5. https://doi.org/10.1126/science.1248506.
Lewandowsky, Stephan, John Cook, Ullrich KH Ecker, Dolores Albarracín, Naomi Kennett, Madalina Vlasceanu, Klaus Oberauer, et al. 2020. “The Debunking Handbook 2020.” Databrary. https://doi.org/10.17910/b7.1182.
Lewandowsky, Stephan, Ullrich KH Ecker, Colleen M Seifert, Norbert Schwarz, and John Cook. 2012. “Misinformation and Its Correction: Continued Influence and Successful Debiasing.” Psychological Science in the Public Interest 13 (3): 106–31. https://doi.org/10.1177/1529100612451018.
Lindsay, Robert K., Bruce G. Buchanan, Edward A. Feigenbaum, and Joshua Lederberg. 1993. “DENDRAL: A Case Study of the First Expert System for Scientific Hypothesis Formation.” Artificial Intelligence 61 (2): 209–61. https://doi.org/10.1016/0004-3702(93)90068-M.
Lipsitch, Marc, David L Swerdlow, and Lyn Finelli. 2020. “Defining the Epidemiology of Covid-19—Studies Needed.” New England Journal of Medicine 382 (13): 1194–96. https://doi.org/10.1056/NEJMp2002125.
Little, Roderick JA, and Donald B Rubin. 2019. Statistical Analysis with Missing Data. 3rd ed. Hoboken, NJ: Wiley. https://doi.org/10.1002/9781119482260.
Liu, Haotian, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023. “Visual Instruction Tuning.” Advances in Neural Information Processing Systems 36. https://arxiv.org/abs/2304.08485.
Liu, Xiaoxuan, Livia Faes, Aditya U Kale, Siegfried K Wagner, Dun Jack Fu, Alice Bruynseels, et al. 2019. “A Comparison of Deep Learning Performance Against Health-Care Professionals in Detecting Diseases from Medical Imaging: A Systematic Review and Meta-Analysis.” The Lancet Digital Health 1 (6): e271–97. https://doi.org/10.1016/S2589-7500(19)30123-2.
Lohr, Steve. 2022. “IBM Sells Watson Health Assets, Ending Era for Business.” The New York Times. https://www.nytimes.com/2022/01/21/technology/ibm-watson-health.html.
Loomba, Sahil, Alexandre de Figueiredo, Simon J Piatek, Kristen de Graaf, and Heidi J Larson. 2021. “Measuring the Impact of COVID-19 Vaccine Misinformation on Vaccination Intent in the UK and USA.” Nature Human Behaviour 5 (3): 337–48. https://doi.org/10.1038/s41562-021-01056-1.
Lundberg, Scott M, and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems 30. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html.
Markus, Aniek F, Jan A Kors, and Peter R Rijnbeek. 2021. “The Role of Explainability in Creating Trustworthy Artificial Intelligence for Health Care: A Comprehensive Survey of the Terminology, Design Choices, and Evaluation Strategies.” Journal of Biomedical Informatics 113: 103655. https://doi.org/10.1016/j.jbi.2020.103655.
McCarthy, John, Marvin L. Minsky, Nathaniel Rochester, and Claude E. Shannon. 2006. “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955.” AI Magazine 27 (4): 12. https://doi.org/10.1609/aimag.v27i4.1904.
McCoy, Liam G, Sai Nagaraj, Fernanda Morgado, Vinyas Harish, Sunit Das, and Leo Anthony Celi. 2020. “What Do Medical Students Actually Need to Know about Artificial Intelligence?” Npj Digital Medicine 3 (1): 86. https://doi.org/10.1038/s41746-020-0294-7.
McGough, Sarah F, Michael A Johansson, Marc Lipsitch, and Nicolas A Menzies. 2020. “Nowcasting by Bayesian Smoothing: A Flexible, Generalizable Model for Real-Time Epidemic Tracking.” PLoS Computational Biology 16 (4): e1007735. https://doi.org/10.1371/journal.pcbi.1007735.
Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. “Efficient Estimation of Word Representations in Vector Space.” arXiv Preprint arXiv:1301.3781.
Miller, Randolph A., Harry E. Pople, and Jack D. Myers. 1982. “INTERNIST-1, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine.” New England Journal of Medicine 307 (8): 468–76. https://doi.org/10.1056/NEJM198208193070803.
Molnar, Christoph. 2024. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 2nd ed. https://christophm.github.io/interpretable-ml-book/.
Mukherjee, Siddhartha. 2021. “What Happened When Google Health Tried to Use AI to Predict Patient Outcomes.” The New Yorker. https://www.newyorker.com/magazine/2021/04/05/what-data-cant-do.
Ng, Andrew. n.d. “Andrew Ng (@AndrewYNg) on x/Twitter.” Twitter/X profile. https://x.com/AndrewYNg.
Nori, Harsha, Nicholas King, Scott Mayer McKinney, Dean Carignan, and Eric Horvitz. 2023. “Capabilities of GPT-4 on Medical Challenge Problems.” arXiv Preprint arXiv:2303.13375.
Obermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019. “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations.” Science 366 (6464): 447–53. https://doi.org/10.1126/science.aax2342.
Olson, Donald R., Kevin J. Konty, Marc Paladini, Cécile Viboud, and Lone Simonsen. 2013. “Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales.” PLoS Computational Biology 9 (10): e1003256. https://doi.org/10.1371/journal.pcbi.1003256.
OpenAI. 2023. “GPT-4 Technical Report.” arXiv Preprint arXiv:2303.08774.
Pearl, Judea. 2009. “Causal Inference in Statistics: An Overview.” Statistics Surveys 3: 96–146. https://doi.org/10.1214/09-SS057.
Pierri, Francesco, Brea L Perry, Matthew R DeVerna, Kai-Cheng Yang, Alessandro Flammini, Filippo Menczer, and John Bryden. 2022. “Vaccination and Social Media: A Systematic Review.” Journal of Medical Internet Research 24 (11): e38977. https://doi.org/10.2196/38977.
Poplin, Ryan, Pi-Chuan Chang, David Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, et al. 2018. “A Universal SNP and Small-Indel Variant Caller Using Deep Neural Networks.” Nature Biotechnology 36 (10): 983–87. https://doi.org/10.1038/nbt.4235.
Powles, Julia, and Hal Hodson. 2017. “Google DeepMind and Healthcare in an Age of Algorithms.” Health and Technology 7 (4): 351–67. https://doi.org/10.1007/s12553-017-0179-1.
Preskill, John. 2018. “Quantum Computing in the NISQ Era and Beyond.” Quantum 2: 79. https://doi.org/10.22331/q-2018-08-06-79.
Rajkomar, Alvin, Jeffrey Dean, and Isaac Kohane. 2019. “Machine Learning in Medicine.” New England Journal of Medicine 380 (14): 1347–58. https://doi.org/10.1056/NEJMra1814259.
Rajpurkar, Pranav, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, et al. 2017. “CheXNet: Radiologist-Level Pneumonia Detection on Chest x-Rays with Deep Learning.” arXiv Preprint arXiv:1711.05225.
Reich, Nicholas G, Logan C Brooks, Spencer J Fox, Sasikiran Kandula, Craig J McGowan, Evan Moore, et al. 2019. “A Collaborative Multiyear, Multimodel Assessment of Seasonal Influenza Forecasting in the United States.” Proceedings of the National Academy of Sciences 116 (8): 3146–54. https://doi.org/10.1073/pnas.1812594116.
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. 2016. “Why Should i Trust You? Explaining the Predictions of Any Classifier.” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–44. https://doi.org/10.1145/2939672.2939778.
Roozenbeek, Jon, Sander van der Linden, Beth Goldberg, Steve Rathje, and Stephan Lewandowsky. 2022. “Psychological Inoculation Improves Resilience Against Misinformation on Social Media.” Science Advances 8 (34): eabo6254. https://doi.org/10.1126/sciadv.abo6254.
Roozenbeek, Jon, Claudia R Schneider, Sarah Dryhurst, John Kerr, Alexandra LJ Freeman, Gabriel Recchia, Anne Marthe Van Der Bles, and Sander Van Der Linden. 2020. “Susceptibility to Misinformation about COVID-19 Around the World.” Royal Society Open Science 7 (10): 201199. https://doi.org/10.1098/rsos.201199.
Ross, Casey, and Ike Swetlitz. 2017. “MD Anderson Benches IBM Watson in Setback for Artificial Intelligence in Medicine.” STAT News. https://www.statnews.com/2017/02/19/md-anderson-benches-ibm-watson-in-setback-for-artificial-intelligence-in-medicine/.
———. 2018. “IBM’s Watson Supercomputer Recommended ’Unsafe and Incorrect’ Cancer Treatments, Internal Documents Show.” STAT News. https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafe-incorrect-treatments/.
Russell, Timothy W, Joel Hellewell, Christopher I Jarvis, Kevin Van Zandvoort, Sam Abbott, Ruwan Ratnayake, et al. 2020. “Using a Delay-Adjusted Case Fatality Ratio to Estimate Under-Reporting.” CMMID Repository. https://doi.org/10.25561/77731.
Senior, Andrew W, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, et al. 2020. “Improved Protein Structure Prediction Using Potentials from Deep Learning.” Nature 577 (7792): 706–10. https://doi.org/10.1038/s41586-019-1923-7.
Shortliffe, Edward H., Randall Davis, Scott G. Axline, Bruce G. Buchanan, Cordell C. Green, and Stanley N. Cohen. 1975. “Computer-Based Consultations in Clinical Therapeutics: Explanation and Rule Acquisition Capabilities of the MYCIN System.” Computers and Biomedical Research 8 (4): 303–20. https://doi.org/10.1016/0010-4809(75)90009-9.
Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, et al. 2016. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529 (7587): 484–89. https://doi.org/10.1038/nature16961.
Singhal, Karan, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, et al. 2023. “Towards Expert-Level Medical Question Answering with Large Language Models.” arXiv Preprint arXiv:2305.09617.
Strickland, Eliza. 2019. “How IBM Watson Overpromised and Underdelivered on AI Health Care.” IEEE Spectrum. https://spectrum.ieee.org/how-ibm-watson-overpromised-and-underdelivered-on-ai-health-care.
Swire, Briony, Adam J Berinsky, Stephan Lewandowsky, and Ullrich KH Ecker. 2017. “The Role of Familiarity in Correcting Inaccurate Information.” Journal of Experimental Psychology: Learning, Memory, and Cognition 43 (12): 1948–61. https://doi.org/10.1037/xlm0000422.
Tan, Teng-Kee, and Kishore Mahbubani. 2021. “Singapore’s Protection from Online Falsehoods and Manipulation Act: A Critique.” Asian Journal of Comparative Law 16 (1): 125–47. https://doi.org/10.1017/asjcl.2021.4.
Thaker, Jagadish, and Shubhangi Ganchoudhuri. 2021. “The Role of Trust in COVID-19 Vaccine Hesitancy and Acceptance Among Black and Hispanic Communities in the United States.” JMIR Public Health and Surveillance 7 (7): e29851. https://doi.org/10.2196/29851.
Topol, Eric. n.d. “Eric Topol (@EricTopol) on x/Twitter.” Twitter/X profile. https://x.com/EricTopol.
Topol, Eric J. 2019. “High-Performance Medicine: The Convergence of Human and Artificial Intelligence.” Nature Medicine 25 (1): 44–56. https://doi.org/10.1038/s41591-018-0300-7.
Touvron, Hugo, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, et al. 2023. “Llama 2: Open Foundation and Fine-Tuned Chat Models.” arXiv Preprint arXiv:2307.09288. https://arxiv.org/abs/2307.09288.
Turing, A. M. 1950. “Computing Machinery and Intelligence.” Mind 59 (236): 433–60. https://doi.org/10.1093/mind/LIX.236.433.
U.S. Food and Drug Administration. 2024. “Artificial Intelligence and Machine Learning (AI/ML) in Medical Devices: Action Plan.” https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices.
Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. “Attention Is All You Need.” In Advances in Neural Information Processing Systems, 30:5998–6008.
Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2018. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harvard Journal of Law & Technology 31 (2): 841–87. https://arxiv.org/abs/1711.00399.
Wang, Xiaosong, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M. Summers. 2017. “ChestX-Ray8: Hospital-Scale Chest x-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2097–2106. IEEE. https://doi.org/10.1109/CVPR.2017.369.
Weiskopf, Nicole Gray, and Chunhua Weng. 2013. “Methods and Dimensions of Electronic Health Record Data Quality Assessment: Enabling Reuse for Clinical Research.” Journal of the American Medical Informatics Association 20 (1): 144–51. https://doi.org/10.1136/amiajnl-2011-000681.
Whitelaw, Sera, Mamas A Mamas, Eric Topol, and Harriette GC Van Spall. 2020. “Applications of Digital Technology in COVID-19 Pandemic Planning and Response.” The Lancet Digital Health 2 (8): e435–40. https://doi.org/10.1016/S2589-7500(20)30142-4.
Wilson, Peter WF, Ralph B D’Agostino, Daniel Levy, Albert M Belanger, Halit Silbershatz, and William B Kannel. 1998. “Prediction of Coronary Heart Disease Using Risk Factor Categories.” Circulation 97 (18): 1837–47. https://doi.org/10.1161/01.CIR.97.18.1837.
Wong, Andrew, Erkin Otles, John P Donnelly, Andrew Krumm, Jeffrey McCullough, Olivia DeTroyer-Cooley, Justin Pestrue, et al. 2021. “External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients.” JAMA Internal Medicine 181 (8): 1065–70. https://doi.org/10.1001/jamainternmed.2021.2626.
World Health Organization. 2021. “Infodemic Management: An Overview of Infodemic Management During COVID-19.” https://www.who.int/publications/i/item/9789240035270.
Yao, Shunyu, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023. “ReAct: Synergizing Reasoning and Acting in Language Models.” In International Conference on Learning Representations. https://arxiv.org/abs/2210.03629.
Yu, Victor L., Bruce G. Buchanan, Edward H. Shortliffe, Sharon M. Wraith, Randall Davis, A. Carlisle Scott, and Stanley N. Cohen. 1979. “Antimicrobial Selection by a Computer: A Blinded Evaluation by Infectious Disease Experts.” JAMA 242 (12): 1279–82. https://doi.org/10.1001/jama.1979.03300120033018.
Yu, Victor L., Lawrence M. Fagan, Sharon M. Wraith, William J. Clancey, A. Carlisle Scott, John Hannigan, et al. 1979. “Evaluating the Performance of a Computer-Based Consultant.” Computer Programs in Biomedicine 9 (1): 95–102. https://doi.org/10.1016/0010-4809(79)90015-4.