Previous Chapter: 8 Conclusion
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

REFERENCES

Abdelhady, A. M., and C. R. Davis. 2023. Plastic surgery and artificial intelligence: How ChatGPT improved operation note accuracy, time, and education. Mayo Clinic Proceedings: Digital Health 1(3):299–308. https://doi.org/10.1016/j.mcpdig.2023.06.002.

Access Now. 2020. Access Now Resigns from the Partnership on AI. https://www.accessnow.org/press-release/access-now-resignation-partnership-on-ai (accessed April 4, 2025).

Adam, G. A., C. K. Chang, B. Haibe-Kains, and A. Goldenberg. 2020. Hidden risks of machine learning applied to healthcare: Unintended feedback loops between models and future data causing model degradation. Proceedings of Machine Learning Research 126:710–731.

Adams, L., E. Fontaine, S. Lin, T. Crowell, V. C. H. Chung, and A. A. Gonzalez. 2024. Artificial intelligence in health, health care, and biomedical science: An AI code of conduct principles and commitments discussion draft. NAM Perspectives. Commentary, National Academy of Medicine, Washington, DC. https://doi.org/10.31478/202403a.

Adler-Milstein, J., N. Aggarwal, M. Ahmed, J. Castner, B. Evans, A. Gonzalez, C. A. James, S. Lin, K. Mandl, M. Matheny, M. Sendak, C. Shachar, and A. Williams. 2022. Meeting the moment: Addressing barriers and facilitating clinical adoption of artificial intelligence in medical diagnosis. NAM Perspectives. Discussion Paper, National Academy of Medicine, Washington, DC. https://doi.org/10.31478/202209c.

Adus, S., J. Macklin, and A. Pinto. 2023. Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care. BMC Health Services Research 23:1163. https://doi.org/10.1186%2Fs12913-023-10098-2.

Advanced Research Projects Agency for Health (ARPA-H). 2024. RFI: Real World Data Sources for Health and Health Care Research. https://arpa-h.gov/news-and-events/rfi-real-world-data-sources-health (accessed April 4, 2025).

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Agency for Healthcare Research and Quality (AHRQ). 2022. Improving Healthcare Through AHRQ’s Digital Healthcare Research Program. https://digital.ahrq.gov/sites/default/files/docs/page/ahrq-dhr-2022-year-in-review.pdf (accessed July 3, 2024).

Aggarwal, N., M. Ahmed, S. Basu, J. J. Curtin, B. J. Evans, M. E. Matheny, S. Nundy, M. P. Sendak, C. Shachar, R. U. Shah, and S. Thadaney-Israni. 2020. Advancing artificial intelligence in health settings outside the hospital and clinic. NAM Perspectives. National Academy of Medicine, Washington, DC. https://doi.org/10.31478/202011f.

Ahmed, M. I., B. Spooner, J. Isherwood, M. Lane, E. Orrock, and A. Dennison. 2023a. A systematic review of the barriers to the implementation of artificial intelligence in healthcare. Cureus 15(10):e46454. https://doi.org/10.7759%2Fcureus.46454.

Ahmed, N., M. Wahed, and N. C. Thompson. 2023b. The growing influence of industry in AI research. Science 379(6635):884–886. https://doi.org/10.1126/science.ade2420.

Aikens, R. C., J. H. Chen, M. Baiocchi, and J. F. Simard. 2024. Feedback loop failure modes in medical diagnosis: How biases can emerge and be reinforced. Medical Decision Making 44(5):481–496. https://doi.org/10.1177/0272989X241248612.

Ajzen, I. 1985. From intentions to actions: A theory of planned behavior. In Action Control: From Cognition to Behavior, edited by J. Kuhl and J. Beckmann. Springer, pp. 11–39.

Alba, A. C., T. Agoritsas, M. Walsh, S. Hanna, A. Iorio, P. J. Devereaux, T. McGinn, and G. Guyatt. 2017. Discrimination and calibration of clinical prediction models: Users’ guides to the medical literature. JAMA 318(14):1377–1384. https://doi.org/10.1001/jama.2017.12126.

Albahri, A. S., A. M. Duhaim, M. A. Fadhel, A. Alnoor, N. S. Baqer, L. Alzubaidi, O. Albahri, A. Alamoodi, J. Bai, A. Salhi, J. I. Santamaría, C. Ouyang, A. Gupta, Y. Gu, and M. Deveci. 2023. A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion 96:156–191. https://doi.org/10.1016/j.inffus.2023.03.008.

Alizadeh, A., M. Eisen, R. Davis, C. Ma, I. Lossos, A. Rosenwald, J. Boldrick, H. Sabet, T. Tran, X. Yu, J. Powell, L. Yang, G. Marti, T. Moore, J. Hudson, Jr., L. Lu, D. Lewis, R. Tibshirani, G. Sherlock, W. Chan, T. Greiner, D. Weisenburger, J. Armitage, R. Warnke, R. Levy, W. Wilson, M. Grever, J. Byrd, D. Botstein, P. Brown, and L. Staudt. 2000. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769):503–511. https://doi.org/10.1038/35000501.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Almeida, V., L. Schertel Mendes, and D. Doneda. 2023. On the development of AI governance frameworks. IEEE Internet Computing 27(1):70–74. https://doi.org/10.1109/MIC.2022.3186030.

Alobayli, F., S. O’Conner, A. Holloway, and K. Cresswell. 2023. Electronic health record stress and burnout among clinicians in hospital settings: A systematic review. Digital Health 9. https://doi.org/10.1177%2F20552076231220241.

American Hospital Association (AHA). 2003. The Patient Care Partnership. https://www.aha.org/system/files/2018-01/aha-patient-care-partnership.pdf (accessed April 4, 2025).

AHA. 2019. Surveying the AI Landscape. https://www.aha.org/system/files/media/file/2019/10/Market_Insights_AI-Landscape.pdf (accessed July 8, 2024).

Anderson, J. G. 2007. Social, ethical and legal barriers to e-health. International Journal of Medical Informatics 76(5–6):480–483. https://doi.org/10.1016/j.ijmedinf.2006.09.016.

Anderson, J., and L. Rainie. 2023. As AI Spreads, Experts Predict the Best and Worst Changes in Digital Life by 2035. Pew Research Center.

Angwin, J. 2023. Autonomous vehicles are driving blind. The New York Times. https://www.nytimes.com/2023/10/11/opinion/driverless-cars-sanfrancisco.html?smid=url-share (accessed April 4, 2025).

Aquino, Y. S. J., W. A. Rogers, A. Braunack-Mayer, H. Frazer, K. T. Win, N. Houssami, C. Degeling, C. Semsarian, and S. M. Carter. 2023. Utopia versus dystopia: Professional perspectives on the impact of healthcare artificial intelligence on clinical roles and skills. International Journal of Medical Informatics 169:104903. https://doi.org/10.1016/j.ijmedinf.2022.104903.

Arnaout, A., M. Oseguera-Arasmou, N. Mishra, B. M. Liu, A. Bhattacharya, and D. C. Rhew. 2023. Leveraging technology in public–private partnerships: A model to address public health inequities. Frontiers in Health Services 3:1187306. https://doi.org/10.3389/frhs.2023.1187306.

Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD). n.d. AIM-AHEAD. https://www.aim-ahead.net (accessed April 4, 2025).

Ash, J. S. 2004. Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. Journal of the American Medical Informatics Association 11(2):104–112. https://doi.org/10.1197/jamia.M1471.

Ashton, M. 2018. Getting rid of stupid stuff. New England Journal of Medicine 379:1789–1791. https://doi.org/10.1056/NEJMp1809698.

Assistant Secretary for Technology Policy, Office of the National Coordinator for Health IT (ASTP ONC). 2024. Vulcan Interoperability Bridge Event to Enable

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Clinical Research Infrastructure Information Session. https://www.healthit.gov/news/events/vulcan-interoperability-bridge-event-enable-clinical-research-infrastructure (accessed April 4, 2025).

ASTP ONC. n.d. Regional Extension Centers (RECs). https://www.healthit.gov/topic/regional-extension-centers-recs (accessed April 4, 2025).

Atherton, J. 2011. Development of the electronic health record. Virtual Mentor 13(3):186–189. https://doi.org/10.1001/virtualmentor.2011.13.3.mhst1-1103.

Attard-Frost, B., and D. G. Widder. 2024. The ethics of AI value chains. arXiv. https://doi.org/10.48550/arXiv.2307.16787.

Awad, E., S. Dsouza, R. Kim, J. Schulz, J. Henrich, A. Shariff, J. F. Bonnefon, and I. Rahwan. 2018. The moral machine experiment. Nature 563:59–64. https://doi.org/10.1038/s41586-018-0637-6.

Ayers, J. W., A. Poliak, M. Dredze, E. C. Leas, Z. Zhu, J. B. Kelley, D. J. Faix, A. M. Goodman, C. A. Longhurst, M. l. Hogarth, and D. M. Smith 2023. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Internal Medicine 183(6):589–596. https://doi.org/10.1001/jamainternmed.2023.1838.

Bajwa, J., U. Munir, A. Nori, and B. Williams. 2021. Artificial intelligence in health care: Transforming the practice of medicine. Future Healthcare Journal 8(2):188–194. https://doi.org/10.7861/fhj.2021-0095.

Band, S., A. Yarahmadi, C. Hsu, M. Biyari, M. Sookhak, R. Ameri, I. Dehzangi, A. T. Chronopoulos, and H. Liang. 2023. Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods. Informatics in Medicine Unlocked 40:101286. https://doi.org/10.1016/j.imu.2023.101286.

Bandura, A. 2006. Toward a psychology of human agency. Perspectives on Psychological Science 1(2):164–180. https://doi.org/10.1111/j.1745-6916.2006.00011.x.

Banja, J., R. D. Hollstein, and M. A. Bruno. 2022. When artificial intelligence models surpass physician performance: Medical malpractice liability in an era of advanced artificial intelligence. Journal of the American College of Radiology 19(7):816–820. https://doi.org/10.1016/j.jacr.2021.11.014.

Bates, D. W., and A. Gawande. 2003. Improving safety with information technology. New England Journal of Medicine 348:2526–2534. https://doi.org/10.1056/NEJMsa020847.

Bates, D. W., and H. Singh. 2018. Two decades since To Err Is Human: An assessment of progress and emerging priorities in patient safety. Health Affairs 37(11):1736–1743. https://doi.org/10.1377/hlthaff.2018.0738.

Bates, D. W., L. L. Leape, D. J. Cullen, N. Laird, L. A. Petersen, J. M. Teich, E. Burdick, M. Hickey, S. Kleefield, B. Shea, M. Vander Vliet, and D. L. Seger. 1998.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA 280(15):1311–1316. https://doi.org/10.1001/jama.280.15.1311.

Bates, D. W., D. Levine, A. Syrowatka, M. Kuznetsova, K. J. T. Craig, A. Rui, G. P. Jackson, and K. Rhee. 2021. The potential of artificial intelligence to improve patient safety: A scoping review. NPJ Digital Medicine 4(1):54. https://doi.org/10.1038/s41746-021-00423-6.

Bayati, M., M. Braverman, M. Gillam, K. M. Mack, G. Ruiz, M. S. Smith, and E. Horvitz. 2014. Data-driven decisions for reducing readmissions for heart failure: General methodology and case study. PLoS ONE 9(10):e109264. https://doi.org/10.1371/journal.pone.0109264.

Bedoya, A. D., N. J. Economou-Zavlanos, B. A. Goldstein, A. Young, J. E. Jelovsek, C. O’Brien, A. B. Parrish, S. Elengold, K. Lytle, S. Balu, E. Huang, E. G. Poon, and M. J. Pencina. 2022. A framework for the oversight and local deployment of safe and high-quality prediction models. Journal of the American Medical Informatics Association 29(9):1631–1636. https://doi.org/10.1093/jamia/ocac078.

Berg, M. 2001. Implementing information systems in health care organizations: Myths and challenges. International Journal of Medical Informatics 64(2–3):143–156. https://doi.org/10.1016/S1386-5056(01)00200-3.

Best, E., P. Robles, and D. J. Mallinson. 2024. The future of AI politics, policy and business. Business and Politics 26(2):171–179. https://doi.org/10.1017/bap.2024.6.

Birkhäuer, J. 2017. Trust in the health care professional and health outcome: A meta-analysis. PLoS ONE 12(2):e0170988. https://doi.org/10.1371/journal.pone.0170988.

Bitterman, D., H. Aerts, and R. Mak. 2020. Approaching autonomy in medical artificial intelligence. Lancet, Digital Health 2(9):e447–e449. https://doi.org/10.1016/S2589-7500(20)30187-4.

Blumenthal, D., and B. Patel. 2024. The regulation of clinical artificial intelligence. NEJM AI 1(8). https://doi.org/10.1056/AIpc2400545.

Blumenthal, D., and M. Tavenner. 2010. The “meaningful use” regulation for electronic health records. New England Journal of Medicine 363(6):501–504. https://doi.org/10.1056/NEJMp1006114.

Borna, S., M. J. Maniaci, C. R. Haider, C. A. Gomez-Cabello, S. M. Pressman, S. A. Haider, B. M. Demaerschalk, J. B. Cowart, and A. J. Forte. 2024. Artificial intelligence support for informal patient caregivers: A systematic review. Bioengineering 11(5):483. https://doi.org/10.3390/bioengineering11050483.

Boskma, A., K. van der Braak, N. Ansari, L. Hooft, G. Wietasch, A. Franx, and M. van der Laan. 2023. Assessing the well-being at work of nurses and doctors

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

in hospitals: Protocol for a scoping review of monitoring instruments. JMIR Research Protocols 12:e43692. https://doi.org/10.2196%2F43692.

Bouderhem, R. 2024. Shaping the future of AI in health care through ethics and governance. Humanities and Social Science Communications 11(416). https://doi.org/10.1057/s41599-024-02894-w.

Bowes, W. A. 2014. Impacts of EHR certification and meaningful use implementation on an integrated delivery network. AMIA Annual Symposium Proceedings 2014:325–332.

Brady, A. P., B. Allen, J. Chong, E. Kotter, N. Kottler, J. Mongan, L. Oakden-Rayner, D. Pinto dos Santos, A. Tang, C. Wald, and J. Slavotinek. 2024. Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA. Canadian Association of Radiologists Journal 75(2):226–244. https://doi.org/10.1177/08465371231222229.

Braithwaite, J., P. Glasziou, and J. Westbrook. 2020. The three numbers you need to know about health care: The 60-30-10 Challenge. BMC Medicine 18:102. https://doi.org/10.1186/s12916-020-01563-4.

Bremmer, I., and M. Suleyman. 2023. Building blocks for AI governance. International Monetary Fund’s F&D 60(4):10–12. https://www.imf.org/en/Publications/fandd/issues/2023/12/POV-building-blocks-for-AI-governance-Bremmer-Suleyman (accessed April 4, 2025).

Brereton, T. A., M. M. Malik, M. Lifson, J. D. Greenwood, K. J. Peterson, and S. M. Overgaard. 2023. The role of artificial intelligence model documentation in translational science: Scoping review. Interactive Journal of Medical Research 12:e45903. https://doi.org/10.2196/45903.

Brown, J. T., Z. Wan, A. Gkoulalas-Divanis, M. Kantarcioglu, and B. A. Malin. 2023. Supporting COVID-19 disparity investigations with dynamically adjusting case reporting policies. AMIA Annual Symposium Proceedings 2022:279–288.

Brown, J. A., B. D. Taffe, J. A. Richmond, and M. L. Roberson. 2024. Racial discrimination and health-care system trust among American adults with and without cancer. Journal of the National Cancer Institute 116(11):1845–1855. https://doi.org/10.1093/jnci/djae154.

Buchanan, B. G., and E. H. Shortliffe. 1984. Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Reading, MA: Addison Wesley.

Budd, J. 2023. Burnout related to electronic health record use in primary care. Journal of Primary Care & Community Health 14. https://doi.org/10.1177%2F21501319231166921.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Buocz, T., S. Pfotenhauer, and I. Eisenberger. 2023. Regulatory sandboxes in the AI Act: Reconciling innovation and safety? Law, Innovation and Technology 15(2):357–389. https://doi.org/10.1080/17579961.2023.2245678.

Caldwell, C., J. Brexler, and T. Gillem. 2005. Lean-Six Sigma for Healthcare: A Senior Leader’s Guide to Improving Cost and Throughput. American Society for Quality.

Carnegie Mellon University Software Engineering Institute. 2021. National AI Engineering Initiative: Scalable AI. https://insights.sei.cmu.edu/documents/608/2021_019_001_735330.pdf (accessed July 8, 2024).

Carroll, S. R., E. Herczog, M. Hudson, K. Russell, and S. Stall.2021. Operationalizing the CARE and FAIR Principles for Indigenous data futures. Scientific Data 8(1):108. https://doi.org/10.1038/s41597-021-00892-0.

Cary, M. P., A. Zink, S. Wei, A. Olson, M. Yan, R. Senior, S. Bessias, K. Gadhoumi, G. Jean-Pierre, D. Wang, L. S. Ledbetter, N. J. Economou-Zavlanos, Z. Obermeyer, and M. J. Pencina. 2023. Mitigating racial and ethnic bias and advancing health equity in clinical algorithms: A scoping review. Health Affairs 42(10):1359–1368. https://doi.org/10.1377/hlthaff.2023.00553.

Centers for Disease Control and Prevention (CDC). 2021. NEHRS Public Use File National Weighted Estimates. https://www.cdc.gov/nchs/data/nehrs/2021NEHRS-PUF-weighted-estimates-508.pdf (accessed April 4, 2025).

CDC. 2022. Looking at AI’s Potential Impact on Health Equity. https://www.cdc.gov/surveillance/data-modernization/snapshot/2022-snapshot/stories/ai-impact-health-equity.html (accessed July 3, 2024).

CDC. 2024. National Diabetes Statistics Report. https://www.cdc.gov/diabetes/php/data-research/index.html (accessed July 13, 2024).

CDC. n.d. The Untreated Syphilis Study at Tuskegee Timeline. https://www.cdc.gov/tuskegee/timeline.htm (accessed July 30, 2024).

Centers for Medicare & Medicaid Services (CMS). 2024. National Health Expenditure Fact Sheet. https://www.cms.gov/data-research/statistics-trends-and-reports/national-health-expenditure-data/nhe-fact-sheet (accessed April 16, 2025).

Chassin, M. R., and R. W. Galvin. 1998. The urgent need to improve health care quality. Institute of Medicine National Roundtable on Health Care Quality. JAMA 280(11):1000–1005. https://doi.org/10.1001/jama.280.11.1000.

Chaudhry, B., J. Wang, and S. Wu. 2006. Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine 144(10):742–752. https://doi.org/10.7326/0003-4819144-10-200605160-00125.

Chen, H., C. Gomez, C. M. Huang, and M. Unberath. 2022. Explainable medical imaging AI needs human-centered design: Guidelines and evidence from a

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

systematic review. NPJ Digital Medicine 5:156. https://doi.org/10.1038/s41746-022-00699-2.

Chen, M., L. Parks Golding, and G. N. Nicla. 2021. Who will pay for AI? Radiology Artificial Intelligence 3(3). https://doi.org/10.1148/ryai.2021210030.

Christiano, A., and A. Neimand. 2018. The science of what makes people care. Stanford Social Innovation Review 16(4):26–33. https://doi.org/10.48558/GW2V-5279.

Coalition for Health AI (CHAI). n.d. Our Workstreams. https://chai.org/workstreams (accessed April 4, 2025).

Cohen, M. F. 2016. Impact of the HITECH financial incentives on EHR adoption in small, physician-owned practices. International Journal of Medical Informatics 94:143–154. https://doi.org/10.1016/j.ijmedinf.2016.06.017.

Coiera, E. W., K. Verspoor, and D. P. Hansen. 2023. We need to chat about artificial intelligence. Medical Journal of Australia 219(3):98–100. https://doi.org/10.5694/mja2.51992.

Compton-Phillips, A. 2019. Spreading at scale: A practical leadership model for change. NEJM Catalyst 1(1). https://doi.org/10.1056/CAT.19.1083.

Consumer Technology Association. 2023. Artificial Intelligence in Health Care: Practices for Identifying and Managing Bias (ANSI/CTA-2116). https://shop.cta.tech/collections/standards/products/artificial-intelligence-in-health-care-practices-for-identifying-and-managing-bias-cta-2116 (accessed July 3, 2024).

Crafts, N. 2021. Artificial intelligence as a general-purpose technology: An historical perspective. Oxford Review of Economic Policy 37(3):521–536. https://doi.org/10.1093/oxrep/grab012.

Cutler, D. 2023. What artificial intelligence means for health care. JAMA Health Forum 4(7):e232652. https://doi.org/10.1001/jamahealthforum.2023.2652.

Dafoe, A. 2018. AI Governance: A Research Agenda. Future of Humanity Institute. https://www.fhi.ox.ac.uk/wp-content/uploads/GovAI-Agenda.pdf (accessed April 4, 2025).

Damiani, G., G. Altamura, M. Zedda, M. C. Nurchis, G. Aulino, A. Heidar Alizadeh, F. Cazzato, G. Della Morte, M. Caputo, S. Grassi, A. Oliva, and D.3.2 Group. 2023. Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: A systematic review. BMJ Open 13(3):e065301. https://doi.org/10.1136/bmjopen-2022-065301.

Daneshjou, R., M. P. Smith, M. D. Sun, V. Rotemberg, and J. Zou. 2021. Lack of transparency and potential bias in artificial intelligence data sets and algorithms: A scoping review. JAMA Dermatology 157:1362–1369. https://doi.org/10.1001/jamadermatol.2021.3129.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Data Science Process Alliance. n.d. The GenAI Lifecycle. https://www.datascience-pm.com/the-genai-life-cycle (accessed April 4, 2025).

Davenport, T., and R. Kalakota. 2019. The potential for artificial intelligence in healthcare. Future Healthcare 6(2):94–98. https://doi.org/10.7861/futurehosp.6-2-94.

Davis, S. E., T. A. Lasko, G. Chen, E. D. Siew, and M. E. Matheny. 2017. Calibration drift in regression and machine learning models for acute kidney injury. Journal of the American Medical Informatics Association 24(6):1052–1061. https://doi.org/10.1093/jamia/ocx030.

Davis, S. E., R. A. Greevy, Jr., T. A. Lasko, C. G. Walsh, and M. E. Matheny. 2020. Detection of calibration drift in clinical prediction models to inform model updating. Journal of Biomedical Informatics 112:103611. https://doi.org/10.1016/j.jbi.2020.103611.

Davis, S. E., C. G. Walsh, and M. E. Matheny. 2022. Open questions and research gaps for monitoring and updating AI-enabled tools in clinical settings. Frontiers in Digital Health 4:958284. https://doi.org/10.3389/fdgth.2022.958284.

Davis, S. E., P. J. Embí, and M. E. Matheny. 2024. Sustainable deployment of clinical prediction tools—A 360° approach to model maintenance. Journal of the American Medical Informatics Association 31(5):1195–1198. https://doi.org/10.1093/jamia/ocae036.

de Man, Y., Y Wieland-Jorna, B. Torensma, K. de Wit, A. L. Francke, M. G. Oosterveld-Vlug, and R. A. Verheij. 2023. Opt-in and opt-out consent procedures for the reuse of routinely recorded health data in scientific research and their consequences for consent rate and consent bias: Systematic review. Journal of Medical Internet Research 25:e42131. https://doi.org/10.2196%2F42131.

De Silva, D., and D. Alahakoon. 2022. An artificial intelligence life cycle: From conception to production. Patterns 3(6):100489. https://doi.org/10.1016/j.patter.2022.100489.

de Vries, N., A. Boone, L. Godderis, J. Bouman, S. Szemik, D. Matranga, and P. de Winter. 2023. The race to retain healthcare workers: A systematic review on factors that impact retention of nurses and physicians in hospitals. Inquiry 60:469580231159318. https://doi.org/10.1177%2F00469580231159318.

Dean, J. 2022. A golden decade of deep learning: Computing systems & applications. Daedalus 151(2):58–74. https://doi.org/10.1162/daed_a_01900.

Department of Health and Human Services (HHS). 1979. The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research. https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html (accessed July 31, 2024).

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

HHS. 1996. Health Insurance Portability and Accountability Act of 1996. https://aspe.hhs.gov/reports/health-insurance-portability-accountability-act-1996 (accessed April 4, 2025).

HHS. 2009. Health Information Technology for Economic and Clinical Health Act (HITECH Act). https://www.hhs.gov/sites/default/files/ocr/privacy/hipaa/understanding/coveredentities/hitechact.pdf (accessed August 2, 2024).

HHS. 2014. Effects of Meaningful Use Functionalities on Health Care Quality, Safety and Efficiency. https://dashboard.healthit.gov/quickstats/pages/FIG-Health-IT-Literature-Review-Summary-of-Author-Sentiments.php (accessed April 4, 2025).

HHS. 2021. Trustworthy AI (TAI) Playbook. https://www.hhs.gov/sites/default/files/hhs-trustworthy-ai-playbook.pdf (accessed July 3, 2024).

HHS. 2023a. HHS Finalizes Rule to Advance Health IT Interoperability and Algorithm Transparency. https://www.hhs.gov/about/news/2023/12/13/hhs-finalizes-rule-to-advance-health-it-interoperability-and-algorithm-transparency.html (accessed July 3, 2024).

HHS. 2023b. Our Epidemic of Loneliness and Isolation. https://www.hhs.gov/sites/default/files/surgeon-general-social-connection-advisory.pdf (accessed April 4, 2025).

HHS. 2024a. HHS Issues New Rule to Strengthen Nondiscrimination Protections and Advance Civil Rights in Health Care. https://www.hhs.gov/about/news/2024/04/26/hhs-issues-new-rule-strengthen-nondiscrimination-protections-advance-civil-rights-health-care.html (accessed July 14, 2024).

HHS. 2024b. HHS Reorganizes Technology, Cybersecurity, Data, and Artificial Intelligence Strategy and Policy Functions. https://www.hhs.gov/about/news/2024/07/25/hhs-reorganizes-technology-cybersecurity-data-artificial-intelligence-strategy-policy-functions.html (accessed April 4, 2025).

HHS. 2024c. Individuals’ right under HIPAA to access their health information, 45 CFR § 164.524. https://www.hhs.gov/hipaa/for-professionals/privacy/guidance/access/index.html (accessed April 4, 2025).

HHS. 2025. U. S. Department of Health and Human Services: Strategic Plan for the Use of Artificial Intelligence in Health, Human Services, and Public Health. https://www.healthit.gov/sites/default/files/2025-01/2025%20HHS%20AI%20Strategic%20Plan_Full_508.pdf (accessed January 13, 2025).

HHS. n.d.a. HHS Artificial Intelligence (AI) Strategy. https://www.hhs.gov/programs/topic-sites/ai/strategy/index.html (accessed July 3, 2024).

HHS. n.d.b. About HHS. https://www.hhs.gov/about/index.html (accessed July 3, 2024).

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Department of Justice. n.d. Title VI of the Civil Rights Act of 1964. https://www.justice.gov/crt/fcs/TitleVI (accessed April 4, 2025).

Dorr, D. A., L. Adams, and P. Embí. 2023. Harnessing the promise of artificial intelligence responsibly. JAMA 329(16):1347–1348. https://doi.org/10.1001/jama.2023.2771.

du Preez, A., S. Bhattacharya, P. Beling, and E. Bowen. 2025. Fraud detection in healthcare claims using machine learning: A systematic review. Artificial Intelligence in Medicine 160:103061. https://doi.org/10.1016/j.artmed.2024.103061.

Duke AI Health. n.d. What Is ABCDS? https://aihealth.duke.edu/algorithm-based-clinical-decision-support-abcds (accessed April 4, 2025).

Echo Wang, H., M. Landers, R. Adams, A. Subbaswamy, H. Kharrazi, D. Gaskin, and S. Saria. 2022. A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models. Journal of the American Medical Informatics Association 29(8):1323–1333. https://doi.org/10.1093/jamia/ocac065.

Eldridge, N., Y. Wang, M. Metersky, S. Eckenrode, J. Mathew, N. Sonnenfeld, J. Perdue-Puli, D. Hunt, P. J. Brady, P. McGann, E. Grace, D. Rodrick, E. Drye, and H. M. Krumholz. 2022. Trends in adverse event rates in hospitalized patients, 2010–2019. JAMA 328(2):173–183. https://doi.org/10.1001/jama.2022.9600.

Emanuel, L., D. Berwick, J. Conway, J. Combes, M. Hatlie, L. Leape, J. Reason, P. Schyve, C. Vincent, and M. Walton. 2008. What exactly is patient safety? In Advances in Patient Safety: New Directions and Alternative Approaches, Vol. 1, edited by K. Henriksen, J. B. Battles, M. A. Keyes, and M. L. Grady. Rockville, MD: Agency for Healthcare Research and Quality.

Embí, P. J. 2021. Algorithmovigilance—advancing methods to analyze and monitor artificial intelligence–driven health care for effectiveness and equity. JAMA Network Open 4(4):e214622. https://doi.org/10.1001/jamanetworkopen.2021.4622.

Escobar, G. J., V. X. Liu, A. Schuler, B. Lawson, J. D. Greene, and P. Kipnis. 2020. Automated identification of adults at risk for in-hospital clinical deterioration. New England Journal of Medicine 383(20):1951–1960. https://doi.org/10.1056/NEJMsa2001090.

Esmaeilzadeh, P., T. Mirzaei, and S. Dharanikota. 2021. Patients’ perceptions toward human–artificial intelligence interaction in health care: Experimental study. Journal of Medical Internet Research 23(11):e25856. https://doi.org/10.2196/25856.

Esteva, A., A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean. 2019. A guide to deep learning in healthcare. Nature Medicine 25(1):24–29. https://doi.org/10.1038/s41591-018-0316-z.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

European Commission. 2019. Ethics Guidelines for Trustworthy AI. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai (accessed April 4, 2025).

European Commission. 2024a. AI for Public Good: EU-U.S. Research Alliance in AI for the Public Good. https://digital-strategy.ec.europa.eu/en/library/ai-public-good-eu-us-research-alliance-ai-public-good (accessed April 4, 2025).

European Commission. 2024b. EU and US Continue Strong Trade and Technology Cooperation at a Time of Global Challenges. https://ec.europa.eu/commission/presscorner/detail/en/IP_24_1827 (accessed April 4, 2025).

European Medicines Agency (EMA). 2021. Guideline on Clinical Evaluation of Medical Devices. https://www.ema.europa.eu/en/human-regulatory-overview/medical-devices (accessed April 4, 2025).

European Observatory on Health Systems and Policies. 2016. Strengthening Health System Governance: Better Policies, Stronger Performance. Open University Press.

Farrar, B., G. Wang, H. Bos, D. Schneider, H. Noel, J. Guo, L. Koester, A. Desai, K. Manson, S. Garfinkel, A. Ptaszek, and M. Dalldorf. 2015. Evaluation of the Regional Extension Center Program: Final Report. Washington, DC: Office of the National Coordinator for Health Information Technology.

Fawzy, A., T. D. Wu, K. Wang, M. L. Robinson, J. Farha, A. Bradke, S. H. Golden, Y. Xu, and B. T. Garibaldi. 2022. Racial and ethnic discrepancy in pulse oximetry and delayed identification of treatment eligibility among patients with COVID-19. JAMA Internal Medicine 182(7):730–738. https://doi.org/10.1001/jamainternmed.2022.1906.

Fehr, J., B. Citro, R. Malpani, C. Lippert, and V. I. Madai. 2024. A trustworthy AI reality-check: The lack of transparency of artificial intelligence products in healthcare. Frontiers in Digital Health 6:1267290. https://doi.org/10.3389/fdgth.2024.1267290.

Feng, J., R. V. Phillips, I. Malenica, A. Bishara, A. E. Hubbard, L. A. Celi, and R. Pirracchio. 2022. Clinical artificial intelligence quality improvement: Towards continual monitoring and updating of AI algorithms in health care. NPJ Digital Medicine 5(1):66. https://doi.org/10.1038/s41746-022-00611-y.

Fernandes, M. S., and J. R. Goldim. 2024. Artificial intelligence and decision making in health: Risks and opportunities. In Multidisciplinary Perspectives on Artificial Intelligence and the Law. Law, Governance and Technology Series, Vol. 58, edited by H. Sousa Antunes, P. M. Freitas, A. L. Oliveira, C. Martins Pereira, E. Vaz de Sequeira, and L. Barreto Xavier. Cham: Springer, pp. 187–205. https://doi.org/10.1007/978-3-031-41264-6_10.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Ferrara, E. 2023. Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies. Science 6(1):3. https://doi.org/10.3390/sci6010003.

Finlayson, S. G., A. Subbaswamy, K. Singh, J. Bowers, A. Kupke, J. Zittrain, I. S. Kohane, and S. Saria. 2021. The clinician and dataset shift in artificial intelligence. New England Journal of Medicine 385(3):283–286. https://doi.org/10.1056/NEJMc2104626.

Fleisher, L. A., and N. J. Economou-Zavlanos. 2024. Artificial intelligence can be regulated using current patient safety procedures and infrastructure in hospitals. JAMA Health Forum 5(6):e241369. https://doi.org/10.1001/jamahealthforum.2024.1369.

Fleisher, L. A., M. Schreiber, D. Cardo, and A. Srinivasan. 2022. Health care safety during the pandemic and beyond—building a system that ensures resilience. New England Journal of Medicine 386(7):609–611. https://doi.org/10.1056%2FNEJMp2118285.

Fuggetta, A. 2000. Software process: A roadmap. Presented at the Conference on the Future of Software Engineering, Limerick, Ireland.

Furukawa, M. F., J. King, and V. Patel. 2015. Physician attitudes on ease of use of EHR functionalities related to meaningful use. American Journal of Managed Care 21(12):e684–e692.

Gartner. n.d. Gartner Hype Cycle. https://www.gartner.com/en/research/methodologies/gartner-hype-cycle?utm_source (accessed January 13, 2025).

Gianni, R., S. Lehtinen, and M. Nieminen. 2022. Governance of responsible AI: From ethical guidelines to cooperative policies. Frontiers in Computer Science 4. https://doi.org/10.3389/fcomp.2022.873437.

Glauberman, G., A. Ito-Fujita, S. Katz, and J. Callahan. 2023. Artificial intelligence in nursing education: Opportunities and challenges. Hawaii Journal of Health and Social Welfare 82(12):302–305.

Gomes, Y. E., M. Chau, H. A. Banwell, and R. S. Causby. 2022. Diagnostic accuracy of the Ottawa ankle rule to exclude fractures in acute ankle injuries in adults: A systematic review and meta-analysis. BMC Musculoskeletal Disorders 23:885. https://doi.org/10.1186/s12891-022-05831-7.

Gonzales, A., G. Guruswamy, and S. R. Smith. 2023. Synthetic data in health care: A narrative review. PLoS Digital Health 2(1):e0000082. https://doi.org/10.1371/journal.pdig.0000082.

Greeley, C., L. Holder, E. E. Nilsson, and M. K. Skinner. 2024. Scalable deep learning artificial intelligence histopathology slide analysis and validation. Scientific Reports 14:26748. https://doi.org/10.1038/s41598-024-76807-x.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Green, L. A., G. Potworowski, A. Day, R. May-Gentile, D. Vibbert, B. Maki, and L. Kiesel. 2015. Sustaining “meaningful use” of health information technology in low-resource practices. Annals of Family Medicine 13(1):17–22. https://doi.org/10.1370/afm.1740.

Greenhalgh, T., J. Wherton, C. Papoutsi, J. Lynch, G. Hughes, C. A’Court, S. Hinder, N. Fahy, R. Procter, and S. Shaw. 2017. Beyond adoption: A new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. Journal of Medical Internet Research 19(11):e367. https://doi.org/10.2196/jmir.8775.

Grother, P., M. Ngan, and K. Hanaoka. 2019. Face Recognition Vendor Test (FRVT) Part 3 (NISTIR 8280). National Institute of Standards and Technology. http://dx.doi.org/10.6028/NIST.IR.8280.

Guillaudeux, M., O. Rousseau, J. Petot, Z. Bennis, C.-A. Dein, T. Goronflot, N. Vince, S. Limou, M. Karakachoff, M. Wargny, and P.-A. Gourraud. 2023. Patient-centric synthetic data generation, no reason to risk re-identification in biomedical data analysis. NPJ Digital Medicine 6(1):37. https://doi.org/10.1038/s41746-023-00771-5.

Gulshan, V., L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410. https://doi.org/10.1001/jama.2016.17216.

Habib, A., A. Lin, and R. W. Grant. 2021. The Epic Sepsis Model falls short—The importance of external validation. JAMA Internal Medicine 181(8):1040–1041. https://doi.org/10.1001/jamainternmed.2021.3333.

Habicht, J., S. Viswanathan, B. Carrington, T. U. Hauser, R. Harper, and M. Rollwage. 2024. Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot. Nature Medicine 30(2):595–602. https://doi.org/10.1038/s41591-023-02766-x.

Halamka, J., and P. Cerrato. 2021. Understanding the role of digital platforms in technology readiness. Regenerative Medicine 16(3):207–213. https://doi.org/10.2217/rme-2020-0135.

Halamka, J., and M. Tripathi. 2017. The HITECH era in retrospect. New England Journal of Medicine 377(10):907–909. https://doi.org/10.1056/NEJMp1709851.

Han, Y. Y., J. Carcillo, S. Venkataraman, R. S. B. Clark, R. S. Watson, T. C. Nguyen, H. Bayir, and R. A. Orr. 2005. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics 116(6):1506–1512.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Haneuse, S. 2016. Distinguishing selection bias and confounding bias in comparative effectiveness research. Medical Care 54(4):e23–e29. https://doi.org/10.1097/mlr.0000000000000011.

Health AI Partnership (HAIP). n.d. Key Decision Points. https://healthaipartnership.org/key-decisions-in-adopting-an-ai-solution (accessed April 4, 2025).

Heckerman, D. E., E. J. Horvitz, and B. N. Nathwani. 1992. Toward normative expert systems: Part I. The Pathfinder project. Methods of Information in Medicine 31(2):90–105.

Heilinger, J. C. 2022. The ethics of AI ethics. A constructive critique. Philosophy & Technology 35:61. https://doi.org/10.1007/s13347-022-00557-9.

Henry, K. E., R. Kornfield, A. Sridharan, R. C. Linton, C. Groh, T. Wang, A. Wu, B. Mutlu, and S. Saria. 2022. Human–machine teaming is key to AI adoption: Clinicians’ experiences with a deployed machine learning system. NPJ Digital Medicine 5(1):97. https://doi.org/10.1038/s41746-022-00597-7.

Hicks, S. A., I. Strümke, V. Thambawita, M. Hammou, M. A. Riegler, P. Halvorsen, and S. Parasa. 2022. On evaluation metrics for medical applications of artificial intelligence. Scientific Reports 12:5979. https://doi.org/10.1038/s41598-022-09954-8.

Hiefner, A. R., P. Constable, K. Ross, D. Sepdham, and J. B. Ventimiglia. 2022. Protecting family physicians from burnout: Meaningful patient-physician relationships are “more than just medicine.” Journal of the American Board of Family Medicine 35(4):716–723. https://doi.org/10.3122/jabfm.2022.04.210441.

Hillestad, R., J. Bigelow, A. Bower, F. Girosi, R. Meili, R. Scoville, and R. Taylor. 2005. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Affairs (Millwood) 24(5):1103–1117. https://doi.org/10.1377/hlthaff.24.5.1103.

Hoff, T., K. Trovato, and A. Kitsakos. 2023. Burnout among family physicians in the United States: A review of the literature. Quality Management in Health Care 33(1):1–11. https://doi.org/10.1097/qmh.0000000000000439.

Hogg, H. D. J., M. Al-Zubaidy, Technology Enhanced Macular Services Study Reference Group, J. Talks, A. K. Denniston, C. J. Kelly, J. Malawana, C. Papoutsi, M. D. Teare, P. A. Keane, F. R. Beyer, and G. Maniatopoulos. 2023. Stakeholder perspectives of clinical artificial intelligence implementation: Systematic review of qualitative evidence. Journal of Medical Internet Research 25:e39742. https://doi.org/10.2196/39742.

Holmgren, A. J., N. L. Downing, D. W. Bates, T. D. Shanafelt, A. Milstein, C. D. Sharp, D. M. Cutler, R. S. Huckman, and K. A. Schulman. 2021. Assessment of electronic health record use between US and non-US health systems. JAMA Internal Medicine 181(2):251–259. https://doi.org/10.1001/jamainternmed.2020.7071.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Holmgren, A. J., N. Hendrix, N. Maisel, J. Everson, A. Bazemore, L. Rotenstein, R. L. Phillips, and J. Adler-Milstein. 2024. Electronic health record usability, satisfaction, and burnout for family physicians. JAMA Network Open 7(8):e2426956. https://doi.org/10.1001/jamanetworkopen.2024.26956.

Hong, G. S., M. Jang, S. Kyung, K. Cho, J. Jeong, G. Y. Lee, K. Shin, K. D. Kim, S. M. Ryu, J. B. Seo, S. M. Lee, and N. Kim. 2023. Overcoming the challenges in the development and implementation of artificial intelligence in radiology: A comprehensive review of solutions beyond supervised learning. Korean Journal of Radiology 24(11):1061–1080. https://doi.org/10.3348/kjr.2023.0393.

Horvitz, E., and T. M. Mitchell. 2024. Scientific progress in artificial intelligence: History, status, and futures. In Realizing the Promise and Minimizing the Perils of AI for Science and the Scientific Community, edited by K. Hall Jamieson, A.-M. Mazza, and W. Kearney. University of Pennsylvania Press.

Horvitz, E. J., J. S. Breese, and M. Henrion. 1988. Decision theory in expert systems and artificial intelligence. International Journal of Approximate Reasoning 2(3):247–302. https://doi.org/10.1016/0888-613X(88)90120-X.

Householder, A., G. Wassermann, A. Manion, and C. King. 2017. The CERT Guide to Coordinated Vulnerability Disclosure (Special Report CMU/SEI-2017-SR-022). Carnegie Mellon University Software Engineering Institute. https://insights.sei.cmu.edu/documents/1945/2017_003_001_503340.pdf (accessed July 3, 2024).

Howard, J. 2022. Algorithms and the future of work. American Journal of Industrial Medicine 65(12):943–952. https://doi.org/10.1002/ajim.23429.

Howell, M. D., G. S. Corrado, and K. B. DeSalvo. 2024. Three epochs of artificial intelligence in health care. JAMA 331(3):242–244. https://doi.org/10.1001/jama.2023.25057.

Ibrahim, H., X. Liu, S. C. Rivera, D. Moher, A. W. Chan, M. R. Sydes, M. J. Calvert, and A. K. Denniston. 2021. Reporting guidelines for clinical trials of artificial intelligence interventions: The SPIRIT-AI and CONSORT-AI guidelines. Trials 22(1):11. https://doi.org/10.1186/s13063-020-04951-6.

Institute of Medicine (IOM). 1991. Computer-Based Patient Record: An Essential Technology for Health Care. Washington, DC: National Academy Press. https://doi.org/10.17226/18459.

IOM. 2000. To Err Is Human: Building a Safer Health System. Washington, DC: National Academy Press.

IOM. 2001. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

IOM. 2002. Medical innovation in the changing healthcare marketplace: Conference summary. In Barriers to Medical Innovation. Washington, DC: The National Academies Press.

IOM. 2003. Key Capabilities of an Electronic Health Record System: Letter Report. Washington, DC: The National Academies Press.

IOM. 2007. Preventing Medication Errors: Quality Chasm Series. Washington, DC: The National Academies Press.

IOM. 2011. Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care. Washington, DC: The National Academies Press.

International Data Corporation (IDC). 2023. The Business Opportunity of AI: How Leading Organizations Around the World Are Using AI to Drive Impact Across Every Industry. https://news.microsoft.com/source/wp-content/uploads/2023/11/US51315823-IG-ADA.pdf (accessed April 4, 2025).

International Organization for Standardization (ISO). 2023. ISO/IEC 42001. https://www.iso.org/obp/ui/en/#iso:std:iso-iec:42001:ed-1:v1:en (accessed April 4, 2025).

ISO. n.d. Health Informatics. https://www.iso.org/sectors/health/health-informatics?utm_campaign=it-link&utm_source=internal (accessed April 4, 2025).

Jabbour, S., D. Fouhey, S. Shepard, T. S. Valley, E. A. Kazerooni, N. Banovic, J. Wiens, and M. W. Sjoding. 2023. Measuring the impact of AI in the diagnosis of hospitalized patients: A randomized clinical vignette survey study. JAMA 330(23):2275–2284. https://doi.org/10.1001/jama.2023.22295.

Jacob, C., N. Brasier, E. Laurenzi, S. Heuss, S. G. Mougiakakou, A. Cöltekin, and M. K. Peter. 2025. AI for IMPACTS framework for evaluating the long-term real-world impacts of AI-powered clinician tools: Systematic review and narrative synthesis. Journal of Medical Internet Research 27:e67485. https://doi.org/10.2196/67485.

Jia, Z., J. Chen, X. Xu, J. Kheir, J. Hu, H. Xiao, S. Peng, X. S. Hu, D. Chen, and Y. Shi. 2023. The importance of resource awareness in artificial intelligence for health care. Nature Machine Intelligence 5:687–698. https://doi.org/10.1038/s42256-023-00670-0.

Jiang, F., Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, and Y. Wang. 2017. Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology 2(4):230–243. https://doi.org/10.1136/svn-2017-000101.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Johnson, K. B., and W. Stead. 2022. Making electronic health records both SAFER and SMARTER. JAMA 328(6):523–524. https://doi.org/10.1001/jama.2022.12243.

Johnson, K. B., M. J. Neuss, and D. E. Detmer. 2021. Electronic health records and clinician burnout: A story of three eras. Journal of the American Medical Informatics Association 28(5):967–973. https://doi.org/10.1093/jamia/ocaa274.

Johnson, L. C., R. Beaton, S. Murphy, and K. Pike. 2000. Sampling bias and other methodological threats to the validity of health survey research. International Journal of Stress Management 7:247–267. https://doi.org/10.1023/A:1009589812697.

Joseph, S., J. Selvaraj, I. Mani, T. Kumaragurupari, X. Shang, P. Mudgil, T. Ravilla, and M. He. 2024. Diagnostic accuracy of artificial intelligence-based automated diabetic retinopathy screening in real-world settings: A systematic review and meta-analysis. American Journal of Ophthalmology 263:214–230. https://doi.org/10.1016/j.ajo.2024.02.012.

Juhn, Y. J., E. Ryu, C. Wi, K. S. King, M. Malik, S. Romero-Brufau, C. Weng, S. Sohn, R. R. Sharp, and J. D. Halamka. 2022. Assessing socioeconomic bias in machine learning algorithms in health care: A case study of the HOUSES index. Journal of the American Medical Informatics Association 29(7):1142–1151. https://doi.org/10.1093%2Fjamia%2Focac052.

Jung, K. H. 2023. Uncover this tech term: Foundation model. Korean Journal of Radiology 24(10):1038–1041. https://doi.org/10.3348/kjr.2023.0790.

Kaelber, D., P. Greco, and R. D. Cebul. 2005. Evaluation of a commercial electronic medical record (EMR) by primary care physicians 5 years after implementation. AMIA Annual Symposium Proceedings 2005:1002.

Kalkman, S., M. Mostert, C. Gerlinger, J. J. M. van Delden, and G. J. M. W. van Thiel. 2019. Responsible data sharing in international health research: A systematic review of principles and norms. BMC Medical Ethics 20(1):21. https://doi.org/10.1186/s12910-019-0359-9.

Kalkman, S., J. van Delden, A. Banerjee, B. Tyl, M. Mostert, and G. van Thiel. 2022. Patients’ and public views and attitudes towards the sharing of health data for research: A narrative review of the empirical evidence. Journal of Medical Ethics 48(1):3–13. https://doi.org/10.1136/medethics-2019-105651.

Kaur, A., A. Budko, K. Liu, E. Eaton, B. Steitz, and K. B. Johnson. 2024. Automating responses to patient portal messages using generative AI. medRxiv. https://doi.org/10.1101/2024.04.25.24306183.

Kemmerling, M., D. Lütticke, and R. H. Schmitt. 2024. Beyond games: A systematic review of neural Monte Carlo tree search applications. Applied Intelligence 54:1020–1046. https://doi.org/10.1007/s10489-023-05240-w.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Kennedy-Moulton, K., S. Miller, P. Persson, M. Rossin-Slater, L. Wherry, and G. Aldana. 2023. Maternal and Infant Health Inequality: New Evidence from Linked Administrative Data (NBER Working Paper 30693). National Bureau of Economic Research. https://doi.org/10.3386/w30693.

Kesavan, P., and C. J. Dy. 2020. Impact of health care reform on technology and innovation. Hand Clinics 36(2):255–262. https://doi.org/10.1016/j.hcl.2020.01.008.

Khalid, N., A. Qayyum, M. Bilal, A. Al-Fuqaha, and J. Qadir. 2023. Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine 158:106848. https://doi.org/10.1016/j.compbiomed.2023.106848.

Khezeli, K., S. Siegel, B. Shickel, T. Ozrazgat-Baslanti, A. Bihorac, and P. Rashidi. 2023. Reinforcement learning for clinical applications. Clinical Journal of the American Society of Nephrology 18(4):521–523. https://doi.org/10.2215/CJN.0000000000000084.

Kim, J. Y., W. Boag, F. Gulamali, A. Hasan, H. D. J. Hogg, M. Lifson, D. Mulligan, M. Patel, I. D. Raji, A. Sehgal, K. Shaw, D. Tobey, A. Valladares, D. Vidal, S. Balu, and M. Sendak. 2023. Organizational governance of emerging technologies: AI adoption in healthcare. arxiv. https://doi.org/10.48550/arXiv.2304.13081.

Kim, J. Y., A. Hasan, K. C. Kellogg, W. Ratliff, S. G. Murray, H. Suresh, A. Valladares, K. Shaw, D. Tobey, D. E. Vidal, M. A. Lifson, M. Patel, I. Deborah Raji, M. Gao, W. Knechtle, L. Tang, S. Balu, and M. P. Sendak. 2024. 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 inequities. PLoS Digital Health 3(5):e0000390. https://doi.org/10.1371%2Fjournal.pdig.0000390.

Kiseleva, A., D. Kotzinos, and P. De Hert. 2022. Transparency of AI in healthcare as a multilayered system of accountabilities: Between legal requirements and technical limitations. Frontiers in Artificial Intelligence 5:879603. http://dx.doi.org/10.3389/frai.2022.879603.

Knapp, A. N., E. Dow, K. Chen, N. C. Khan, D. V. Do, V. Mahajan, P. Mruthyunjaya, T. Leng, and D. Myung. 2023. Real world outcomes from artificial intelligence to detect diabetic retinopathy in the primary care setting: 12 month experience. Investigative Ophthalmology & Visual Science 64(8):252.

Koller, D., A. Beam, A. Manrai, E. Ashley, X. Liu, J. Gichoya, C. Holmes, J. Zou, N. Dagan, T. Y. Wong, D. Blumenthal, and I. Kohane. 2024. Why we support and encourage the use of large language models in NEJM AI submissions. NEJM AI 1(1). https://doi.org/10.1056/AIe2300128.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Komorowski, M., L. A. Celi, O. Badawi, A. C. Gordon, and A. Aldo Faisal. 2018. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine 24:1716–1720. https://doi.org/10.1038/s41591-018-0213-5.

Kore, A., E. A. Bavil, V. Subasri, M. Abdalla, B. Fine, E. Dolatabadi, and M. Abdalla. 2024. Empirical data drift detection experiments on real-world medical imaging data. Nature Communications. 15:1887. https://doi.org/10.1038/s41467-024-46142-w.

Krittanawong, C., and S. Kaplin. 2021. Artificial intelligence in global health. European Heart Journal 42(24):2321–2322. https://doi.org/10.1093/eurheartj/ehab036.

Krive, J., M. Isola, L. Chang, T. Patel, M. Anderson, and R. Sreedhar. 2023. Grounded in reality: Artificial intelligence in medical education. JAMIA Open 6(2):ooad037. https://doi.org/10.1093/jamiaopen/ooad037.

Kshetri, N. 2024. Economics of artificial intelligence governance. Computer 57(4):113–118. https://doi.org/10.1109/MC.2024.3357951.

Kumar, Y., A. Koul, R. Singla, and M. F. Ijaz. 2023. Artificial intelligence in disease diagnosis: A systematic literature review, synthesizing framework and future research agenda. Journal of Ambient Intelligence and Humanized Computing 14:8459–8486. https://doi.org/10.1007/s12652-021-03612-z.

Kuo, R. Y. L., A. Freethy, J. Smith, R. Hill, J. C, D. Jerome, E. Harriss, G. S. Collins, E. Tutton, and D. Furniss. 2024. Stakeholder perspectives towards diagnostic artificial intelligence: A co-produced qualitative evidence synthesis. eClinicalMedicine 71:102555. https://doi.org/10.1016%2Fj.eclinm.2024.102555.

Kurniawan, M. H., H. Handiyani, T. Nuraini, R. T. S. Hariyati, and S. Sutrisno. 2024. A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness. Annals of Medicine 56(1). https://doi.org/10.1080/07853890.2024.2302980.

Kwong, J. C. C., G. C. Nickel, S. C. Y. Wang, and J. C. Kvedar. 2024. Integrating artificial intelligence into healthcare systems: More than just the algorithm. NPJ Digital Medicine 7(1):52. https://doi.org/10.1038/s41746-024-01066-z.

Lasko, T. A., E. V. Strobl, and W. W. Stead. 2024. Why do probabilistic clinical models fail to transport between sites. NPJ Digital Medicine 7:53. https://doi.org/10.1038/s41746-024-01037-4.

Leckenby, E., D. Dawoud, J. Bouvy, and P. Jónsson. 2021. The sandbox approach and its potential for use in health technology assessment: A literature review. Applied Health Economics and Health Policy 19(6):857–869. https://doi.org/10.1007/s40258-021-00665-1.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Ledley, R. S., and L. B. Lusted 1959. Reasoning foundations of medical diagnosis. Science 130:9–21. https://doi.org/10.1126/science.130.3366.9.

Lee, J., S. Patel, and A. Taxter. 2023. How to make the electronic health record your friend. Current Opinion in Pediatrics 35(5):579–584. https://doi.org/10.1097/MOP.0000000000001261.

Leslie, D., A. Mazumder, A. Peppin, M. K. Wolters, and A. Hagerty. 2021. Does “AI” stand for augmenting inequality in the era of covid-19 healthcare? BMJ 372:304. https://doi.org/10.1136/bmj.n304.

Levin, C., E. Naimi, and M. Saban. 2024. Evaluating GenAI systems to combat mental health issues in healthcare workers: An integrative literature review. International Journal of Medical Informatics 191:105566. https://doi.org/10.1016/j.ijmedinf.2024.105566.

Levy, D. R., M. Hobensack, K. Cato, D. E. Detmer, K. B. Johnson, J. Williamson, J. Murphy, A. Moy, J. Withall, R. Lee, S. C. Rossetti, and S. T. Rosenbloom. 2022. 25 × 5 Symposium to reduce documentation burden: Report-out and call for action. Applied Clinical Informatics 13(2):439–446. https://doi.org/10.1055/s-0042-1746169.

Li, F., N. Ruijs, and Y. Lu. 2022. Ethics & AI: A systematic review on ethical concerns and related strategies for designing with AI in health care. AI 4(1):28–53. https://doi.org/10.3390/ai4010003.

Li, J., B. J. Cairns, J. Li, and T. Zhu. 2023. Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications. NPJ Digital Medicine 6(1):98. https://doi.org/10.1038/s41746-023-00834-7.

Lindsell, C. J., W. W. Stead, and K. B. Johnson. 2020. Action-informed artificial intelligence—Matching the algorithm to the problem. JAMA 323(21):2141–2142. https://doi.org/10.1001/jama.2020.5035.

Lion, K. C., Y. Lin, and T. Kim. 2024. Artificial intelligence for language translation: The equity is in the details. JAMA 332(17):1427–1428. https://doi.org/10.1001/jama.2024.15296.

Liu, J., C. Wang, and S. Liu. 2023. Utility of chatGPT in clinical practice. Journal of Medical Internet Research 25:e48568. https://doi.org/10.2196/48568.

Liu, K., and D. Tao. 2022. The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart health care services. Computers in Human Behavior 127:107026. https://doi.org/10.1016/j.chb.2021.107026.

Lohr, S. 2005. Health industry under pressure to computerize. The New York Times. https://www.nytimes.com/2005/02/19/business/health-industry-under-pressure-to-computerize.html?searchResultPosition=1 (accessed December 9. 2024).

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Lopes, L., A. Montero, M. Presiado, and L. Hamel. 2024. Americans’ challenges with health care costs. KFF Issue Brief. https://www.kff.org/health-costs/issue-brief/americans-challenges-with-health-care-costs (accessed April 4, 2025).

Lorenz, E. N. 1972. Predictability: Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas? Presented at the 139th Annual Meeting of the American Association for the Advancement of Science.

Lucian Leape Institute. 2024. Patient Safety and Artificial Intelligence: Opportunities and Challenges for Care Delivery. Institute for Healthcare Improvement. https://www.ihi.org/resources/publications/patient-safety-and-artificial-intelligence-opportunities-and-challenges-care (accessed April 4, 2025).

Lynch, K., M. Kendall, K. Shanks, A. Haque, E. Jones, M. G. Wanis, M. Furukawa, and F. Mostashari. 2014. The health IT regional extension center program: Evolution and lessons for health care transformation. Health Services Research 49:421–437. https://doi.org/10.1111/1475-6773.12140.

Malgaroli, M., E. Tseng, T. D. Hull, E. Jennings, T. K. Choudhury, and N. M. Simon. 2023. Association of health care work with anxiety and depression during the COVID-19 pandemic: Structural topic modeling study. JMIR AI 2:e47223. https://doi.org/10.2196/47223.

Mandl, K. D., D. Gottlieb, and J. C. Mandel. 2024. Integration of AI in health care requires an interoperable digital data ecosystem. Nature Medicine 30:631–634. https://doi.org/10.1038/s41591-023-02783-w.

Mäntymäki, M., M. Minkkinen, T. Birkstedt, and M. Viljanen. 2022. Defining organizational AI governance. AI and Ethics 2:603–609. https://doi.org/10.1007/s43681-022-00143-x.

Matheny, M., D. Whicher, and S. Thadaney Israni. 2020. Artificial intelligence in health care: A report from the National Academy of Medicine. JAMA 323(6):509–510. https://doi.org/10.1001/jama.2019.21579.

Mazzucato, M., M. Schaake, S. Krier, and J. Entsminger. 2022. Governing Artificial Intelligence in the Public Interest. Working Paper Series, UCL Institute for Innovation and Public Purpose.

McCarthy, J., M. L. Minsky, N. Rochester, and C. E. Shannon. 1955. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. https://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html (accessed December 17, 2024).

McCoy, A. B., E. M. Russo, K. B. Johnson, B. Addison, N. Patel, J. P. Wanderer, D. E. Mize, J. G. Jackson, T. J. Reese, S. Littlejohn, L. Patterson, T. French, D. Preston, A. Rosenbury, C. Valdez, S. D. Nelson, C. V. Aher, M. W. Alrifai, J. Andrews, C. Cobb, S. N. Horst, D. P. Johnson, L. A. Knake, A. A. Lewis, L. Parks, S. K. Parr, P. Patel, B. L. Patterson, C. M. Smith, K. D. Suszter, R. W. Turer, L. J. Wilcox,

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

A. P. Wright, and A. Wright. 2022. Clinician collaboration to improve clinical decision support: The ClickBusters initiative. Journal of the American Medical Informatics Association 29(6):1050–1059. https://doi.org/10.1093/jamia/ocac027.

McCradden, M. D., S. Joshi, J. A. Anderson, and A. J. London. 2023. A normative framework for artificial intelligence as a sociotechnical system in healthcare. Patterns 4(11):100864. https://doi.org/10.1016/j.patter.2023.100864.

McGenity, C., E. L. Clarke, C. Jennings, G. Matthews, C. Cartlidge, H. Freduah-Agyemang, D. D. Stocken, and D. Treanor. 2024. Artificial intelligence in digital pathology: A systematic review and meta-analysis of diagnostic test accuracy. NPJ Digital Medicine 7:114. https://doi.org/10.1038/s41746-024-01106-8.

McGinnis, J. M., H. V. Fineberg, and V. J. Dzau. 2024. Shared commitments for health and health care: A trust framework from the Learning Health System. NAM Perspectives. Commentary, Washington, DC. National Academy of Medicine. https://doi.org/10.31478/202412c.

McGlynn, E. A. 2020. Measuring and improving quality in the US: Where are we today? Journal of the American Board of Family Medicine 33(Suppl):S28–S35. https://doi.org/10.3122/jabfm.

Mello, M., and N. Guha. 2024. Understanding liability risk from using health care artificial intelligence tools. New England Journal of Medicine 271–278.

Melnick, E. R., L. N. Dyrbye, C. A. Sinsky, M. Trockel, C. P. West, L. Nedelec, M. A. Tutty, and T. Shanafelt. 2020. The association between perceived electronic health record usability and professional burnout among US physicians. Mayo Clinic Proceedings 95(3):476–487. https://doi.org/10.1016/j.mayocp.2019.09.024.

Mikkelsen, J. G., N. L. Sorensen, C. H. Merrild, M. B. Jensen, and J. L. Thomsen. 2023. Patient perspectives on data sharing regarding implementing and using artificial intelligence in general practice—A qualitative study. BMC Health Services Research 23:335. https://doi.org/10.1186%2Fs12913-023-09324-8.

Mills, S., A. Casovan, and V. Shankar. 2023. A Guide to AI Governance for Business Leaders. Boston Consulting Group. https://www.bcg.com/publications/2023/a-guide-to-mitigating-ai-risks (accessed April 4, 2025).

Mirsky, Y., and W. Lee. 2021. The creation and detection of deepfakes: A survey. ACM Computing Surveys (CSUR) 54(1):7.

MITRE. 2024. Artificial Intelligence Maturity Model. https://www.mitre.org/news-insights/fact-sheet/artificial-intelligence-maturity-model (accessed April 4, 2025).

Mortensen, O. 2024. How Many Users Does ChatGPT Have? Statistics & Facts. SEO News. https://seo.ai/blog/how-many-users-does-chatgpt-have (accessed July 9, 2024).

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Moy, A. J., J. M. Schwartz, R. Chen, S. Sadri, E. Lucas, K. D. Cato, and S. Collins Rossetti. 2021. Measurement of clinical documentation burden among physicians and nurses using electronic health records: A scoping review. Journal of the American Medical Informatics Association 28(5):998–1008. https://doi.org/10.1093/jamia/ocaa325.

Mullowney, M. W., K. R. Duncan, S. S. Elsayed, N. Garg, J. J. J. van der Hooft, N. I. Martin, D. Meijer, B. R. Terlouw, F. Biermann, K. Blin, J. Durairaj, M. Gorostiola González, E. J. N. Helfrich, F. Huber, S. Leopold-Messer, K. Rajan, T. de Rond, J. A. van Santen, M. Sorokina, M. J. Balunas, M. A. Beniddir, D. A. van Bergeijk, L. M. Carroll, C. M. Clark, D.-A. Clevert, C. A. Dejong, C. Du, S. Ferrinho, F. Grisoni, A. Hofstetter, W. Jespers, O. V. Kalinina, S. A. Kautsar, H. Kim, T. F. Leao, J. Masschelein, E. R. Rees, R. Reher, D. Reker, P. Schwaller, M. Segler, M. A. Skinnider, A. S. Walker, E. L. Willighagen, B. Zdrazil, N. Ziemert, R. J. M. Goss, P. Guyomard, A. Volkamer, W. H. Gerwick, H. U. Kim, R. Müller, G. P. van Wezel, G. J. P. van Westen, A. K. H. Hirsch, R. G. Linington, S. L. Robinson, and M. H. Medema. 2023. Artificial intelligence for natural product drug discovery. Nature Reviews 22:895–916. https://doi.org/10.1038/s41573-023-00774-7.

Mumtaz, H., M. K. Ejaz, M. Tayyab, L. Vohra, S. Sapkota, M. Hasan, and M. Saqib. 2023. APACHE scoring as an indicator of mortality rate in ICU patients: A cohort study. Annals of Medicine and Surgery (London) 85(3):416–421. https://doi.org/10.1097/ms9.0000000000000264.

Murdoch, B. 2021. Privacy and artificial intelligence: Challenges for protecting health information in a new era. BMC Medical Ethics 22:122. https://doi.org/10.1186/s12910-021-00687-3.

Muro, M., and S. Liu. 2021. How to Prevent a Winner-Take-Most Outcome for the U. S. AI Economy. The Brookings Institution. https://www.brookings.edu/articles/how-to-prevent-a-winner-take-most-outcome-for-the-u-s-ai-economy (accessed April 4, 2025).

Nan, J., M. S. Herbert, S. Purpura, A. N. Henneken, D. Ramanathan, and J. Mishra. 2024. Personalized machine learning-based prediction of wellbeing and empathy in healthcare professionals. Sensors (Basel) 24(8):2640. https://doi.org/10.3390%2Fs24082640.

National Academy of Medicine (NAM). 2019. Taking Action Against Clinician Burnout: A Systems Approach to Professional Well-Being. Washington, DC: The National Academies Press.

NAM. 2022a. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. Washington, DC: The National Academies Press.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

NAM. 2022b. Assessing Meaningful Community Engagement: A Conceptual Model to Advance Health Equity Through Transformed Systems for Health. Perspectives. Commentary. https://doi.org/10.31478/202202c.

NAM. 2022c. National Plan for Health Workforce Well-Being. Washington, DC: The National Academies Press.

NAM. n.d. Health Care Artificial Intelligence Code of Conduct. https://nam.edu/programs/value-science-driven-health-care/health-care-artificial-intelligence-code-of-conduct (accessed July 11, 2024).

National Association for Healthcare Quality (NAHQ). 2022. Healthcare Quality and Safety Workforce Report: New Imperatives for Quality and Safety Mean New Imperatives for Workforce Development. https://nahq.org/nahq-intelligence/insights/#report (accessed July 30, 2024).

National Conference of State Legislatures. 2025. Artificial Intelligence 2025 Legislation. https://www.ncsl.org/technology-and-communication/artificial-intelligence-2025-legislation (accessed June 4, 2025).

National Institutes of Health (NIH). 2024. Toward an Ethical Framework for Artificial Intelligence in Biomedical and Behavioral Research: Transparency for Data and Model Reuse. https://datascience.nih.gov/sites/default/files/NIH-Transparency-Workshop-Report-v7-FINAL-updated-2-12-11-24-508.pdf (accessed December 12, 2024).

National Research Council (NRC). 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press.

National Science Foundation (NSF). n.d. Artificial Intelligence. https://new.nsf.gov/focus-areas/artificial-intelligence (accessed April 4, 2025).

NEJM-AI Grand Rounds. 2023. Examining Open-Source Frameworks and AI in Medicine with Interventional Radiologist Dr. Judy Gichoya. Narrated by Arjun Manrai and Andrew Beam. https://nejm.ai/ep12 (accessed April 4, 2025).

Nijor, S., G. Rallis, N. Lad, and E. Gokcen. 2022. Patient safety issues from information overload in electronic medical records. Journal of Patient Safety 18(6):999–1003. https://doi.org/10.1097%2FPTS.0000000000001002.

Nilsen, P., D. Sundemo, F. Heintz, M. Neher, J. Nygren, P. Svedberg, and L. Petersson. 2024. Towards evidence-based practice 2.0: Leveraging artificial intelligence in health care. Frontiers in Health Services 4:1368030. https://doi.org/10.3389/frhs.2024.1368030.

Nobel Prize Outreach AB. 2024. The Nobel Prize. Press release. https://www.nobelprize.org/prizes/physics/2024/press-release (accessed December 9, 2024).

NORC. 2021. Surveys of Trust in the U. S. Health Care System. https://www.norc.org/content/dam/norc-org/pdfs/20210520_NORC_ABIM_Foundation_Trust%20in%20Healthcare_Part%201.pdf (accessed April 4, 2025).

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Nori, H., N. King, S. M. McKinney, D. Carignan, and E. Horvitz. 2023. Capabilities of GPT-4 on medical challenge problems. arXiv. https://doi.org/10.48550/arXiv.2303.13375.

Obermeyer, Z., and E. J. Emanuel. 2016. Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine 375(13):1216–1219. https://doi.org/10.1056/nejmp1606181.

Obermeyer, Z., B. Powers, C. Vogeli, and S. Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447–453. https://doi.org/10.1126/science.aax2342.

O’Brien, M. 2024. US Ahead in AI Innovation, Easily Surpassing China in Stanford’s New Ranking. AP News, November 21. https://apnews.com/article/ai-us-china-competition-stanford-index-uk-india-c8eb9be0253eb39776c3e38d05f1a329 (accessed April 4, 2025).

Office for Civil Rights (OCR). n.d. Breach Portal: Notice to the Secretary of HHS breach of unsecured protected health information. https://ocrportal.hhs.gov/ocr/breach/breach_report.jsf;jsessionid=918B4E4145778C463E32915B79A40F34 (accessed April 4, 2025).

Office of Inspector General, Department of Health and Human Services (OIG HHS). 2022. Adverse Events in Hospitals: A Quarter of Medicare Patients Experienced Harm in October 2018 (OEI-06-18-00400). https://oig.hhs.gov/oei/reports/OEI-06-18-00400.pdf (accessed April 4, 2025).

Office of the National Coordinator for Health Information Technology (ONC). 2010. Health information technology: Initial set of standards, implementation specifications, and certification criteria for electronic health record technology. Final rule, Federal Register 75:44590–44654.

ONC. 2011. Establishment of the permanent certification program for health information technology. Final rule. Federal Register 76:1262–1331.

ONC. 2016. ONC health IT certification program: Enhanced oversight and accountability. Final rule. Federal Register 81:72404–72471.

ONC. 2024a. Health data, technology, and interoperability: Certification program updates, algorithm transparency, and information sharing. Final rule. Federal Register 89:1192–1438.

ONC. 2024b. HTI-1 final rule overview. Presented at the HITAC Meeting. https://www.healthit.gov/sites/default/files/facas/2024-01-18_HTI-1_Final_Rule_Overview_508.pdf (accessed April 4, 2025).

ONC. n.d. National Trends in Hospital and Physician Adoption of Electronic Health Records. https://www.healthit.gov/data/quickstats/national-trends-hospital-and-physician-adoption-electronic-health-records (accessed July 23, 2024).

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Oikonomou, E. K., and R. Khera. 2024. Designing medical artificial intelligence systems for global use: Focus on interoperability, scalability, and accessibility. Hellenic Journal of Cardiology 81:9–17. https://doi.org/10.1016/j.hjc.2024.07.003.

Olawade, D. B., O. Z. Wada, A. Odetayo, A. C. David-Olawade, F. Asaolu, and J. Eberhardt. 2024. Enhancing mental health with artificial intelligence: Current trends and future prospects. Journal of Medicine, Surgery, and Public Health 3:100099. https://doi.org/10.1016/j.glmedi.2024.100099.

O’Malley, A., K. Draper, R. Gourevitch, D. Cross, and S. H. Scholle. 2015. Electronic health records and support for primary care teamwork. Journal of the American Medical Informatics Association 22(2):426–434. https://doi.org/10.1093/jamia/ocu029.

Organisation for Economic Co-operation and Development (OECD). 2023. Regulatory sandboxes in artificial intelligence. OECD Digital Economy Papers 356. https://doi.org/10.1787/8f80a0e6-en.

OECD. 2024. OECD AI Principles Overview. https://oecd.ai/en/ai-principles (accessed April 4, 2025).

O’Sullivan, L., R. Crowley, E. McAuliffe, and P. Doran. 2020. Contributory factors to the evolution of the concept and practice of informed consent in clinical research: A narrative review. Contemporary Clinical Trials Communications 19:100634. https://doi.org/10.1016/j.conctc.2020.100634.

Overgaard, S. M., M. G. Graham, T. Brereton, M. J. Pencina, J. D. Halamka, D. E. Vidal, and N. J. Economou-Zavlanos. 2023. Implementing quality management systems to close the AI translation gap and facilitate safe, ethical, and effective health AI solutions. NPJ Digital Medicine 6:218. https://doi.org/10.1038/s41746-023-00968-8.

Parag, N., R. Govender, and S. B. Ally. 2023. Promoting cultural inclusivity in healthcare artificial intelligence: A framework for ensuring diversity. Health Management, Policy and Innovation 8:3.

Paranjape, K., M. Schinkel, R. Nannan Panday, J. Car, and P. Nanayakkara. 2019. Introducing artificial intelligence training in medical education. JMIR Medical Education 5(2):e16048. https://doi.org/10.2196/16048.

Parikh, R. B., and L. A. Helmchen. 2022. Paying for artificial intelligence in medicine. NPJ Digital Medicine 5:63. https://doi.org/10.1038/s41746-022-00609-6.

Patient-Centered Outcomes Research Institute (PCORI). n.d. Engagement in Research: PCORI’s Foundational Expectations for Partnerships. https://www.pcori.org/engagement/engagement-resources/engagement-research-pcoris-foundational-expectations-partnerships (accessed April 4, 2025).

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Patterson, A. M., M. O’Boyle, G. E. VanNoy, and K. A. Dies. 2023. Emerging roles and opportunities for rare disease patient advocacy groups. Therapeutic Advances in Rare Disease 4. https://doi.org/10.1177%2F26330040231164425.

Pearce, R., Y. Li, G. S. Omenn, and Y. Zhang. 2022. Fast and accurate Ab Initio Protein structure prediction using deep learning potentials. PLoS Computational Biology 18(9):e1010539. https://doi.org/10.1371/journal.pcbi.1010539.

Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco, CA: Morgan Kaufmann.

Peccoralo, L., C. Kaplan, R. Pietrzak, D. Charney, and J. Ripp. 2021. The impact of time spent on the electronic health record after work and of clerical work on burnout among clinical faculty. Journal of the American Medical Informatics Association 28(5):938–947. https://doi.org/10.1093/jamia/ocaa349.

Perković, G., A. Drobnjak, and I. Botički. 2024. Hallucinations in LLMs: Understanding and addressing challenges. 47th MIPRO ICT and Electronics Convention (MIPRO). Opatija, Croatia. IEEE, pp. 2084–2088. https://doi.org/10.1109/MIPRO60963.2024.10569238.

Perni, S., L. S. Lehmann, and D. S. Bitterman. 2023. Patients should be informed when AI systems are used in clinical trials. Nature Medicine 29(8):1890–1891. https://doi.org/10.1038/s41591-023-02367-8.

Petersson, L., I. Larsson, J. Nguyen, P. Nilsen, M. Neher, J. E. Reed, D. Tyskbo, and P. Svedberg. 2022. Challenges to implementing artificial intelligence in health care: A qualitative interview study with health care leaders in Sweden. BMC Health Services Research 22(1):850. https://doi.org/10.1186/s12913-022-08215-8.

Phillips, J., and J. D. Klein. 2023. Change management: From theory to practice. TechTrends 67(1):189–197. https://doi.org/10.1007/s11528-022-00775-0.

Pittman, P., C. Chen, C. Erikson, Q. Luo, A. Vichare, S. Batra, and G. Burke. 2021. Health workforce for health equity. Medical Care 59:405–408. https://doi.org/10.1097%2FMLR.0000000000001609.

Placani, A. 2024. Anthropomorphism in AI: Hype and fallacy. AI Ethics 4:691–698. https://doi.org/10.1007/s43681-024-00419-4.

Pouyan, E.2024. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artificial Intelligence in Medicine 151:102861. https://doi.org/10.1016/j.artmed.2024.102861.

Pylypchuk, Y., and C. Johnson. 2022. New EHR certification requirements and their association with duplicate tests and images. Journal of the American Medical Informatics Association 29(8):1391–1399. http://dx.doi.org/10.1093/jamia/ocac076.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Qiang, J., D. Wu, H. Du, H. Zhu, S. Chen, and H. Pan. 2022. Review on facial-recognition-based applications in disease diagnosis. Journal of Personalized Medicine 9(7):273. http://dx.doi.org/10.3390/bioengineering9070273.

Quinn, T. P., M. Senadeera, S. Jacobs, S. Coghlan, and V. Le. 2021. Trust and medical AI: The challenges we face and the expertise needed to overcome them. Journal of the American Medical Informatics Association 28(4):890–894. https://doi.org/10.1093/jamia/ocaa268.

Rajaguru, V., W. Han, T. H. Kim, I. Shin, and S. G. Lee. 2022. LACE index to predict the high risk of 30-day readmission: A systematic review and meta-analysis. Journal of Personalized Medicine 12(4):545. https://doi.org/10.3390/jpm12040545.

Raji, I. D., A. Smart, R. N. White, M. Mitchell, T. Gebru, B. Hutchinson, J. Smith-Loud, D. Theron, and P. Barnes. 2020. Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. New York: Association for Computing Machinery, pp. 33–44. https://doi.org/10.1145/3351095.3372873.

Rajkomar, A., J. Dean, and I. Kohane. 2019. Machine learning in medicine. New England Journal of Medicine 380(14):1347–1358. https://doi.org/10.1056/nejmra1814259.

Ratwani, R. M., D. Classen, and C. Longhurst. 2024. The compelling need for shared responsibility of AI oversight: Lessons from health IT certification. JAMA 332(10):787–788. https://doi.org/10.1001/jama.2024.12630.

Reddy, S. 2024. Generative AI in healthcare: An implementation science informed translational path on application, integration and governance. Implementation Science 19:27. https://doi.org/10.1186/s13012-024-01357-9.

Reddy, S. n.d. The impact of AI on the health care workforce: Balancing opportunities and challenges. Health Information Management Systems Society. https://www.himss.org/resources/impact-ai-healthcare-workforce-balancing-opportunities-and-challenges (accessed July 14, 2024).

Reddy, S., W. Rogers, V. Makinen, E. Coiera, P. Brown, M. Wenzel, E. Weicken, S. Ansari, P. Mathur, A. Casey, and B. Kelly. 2021. Evaluation framework to guide implementation of AI systems into healthcare settings. BMJ Health Care Informatics 28(1):e100444. https://doi.org/10.1136/bmjhci-2021-100444.

Reed, M., J. Huang, I. Graetz, R. Brand, J. Hsu, B. Fireman, and M. Jaffe. 2012. Outpatient electronic health records and the clinical care and outcomes of patients with diabetes mellitus. Annals of Internal Medicine 157(7):482–489. https://doi.org/10.7326/0003-4819-157-7-201210020-00004.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Riddell, M. C., K. G. Sandford, A. O. Johnson, C. Steltenkamp, and K. A. Pearce. 2014. Achieving meaningful use of electronic health records (EHRs) in primary care: Proposed critical processes from the Kentucky Ambulatory Network (KAN). Journal of the American Board of Family Medicine 27(6):772–779. https://doi.org/10.3122/jabfm.2014.06.140030.

Roberts, H., E. Hine, M. Taddeo, and L. Floridi. 2024. Global AI governance—barriers and pathways forward. International Affairs 1275–1286. https://doi.org/10.1093/ia/iiae073.

Robinson, K. E., and J. A. Kersey. 2018. Novel electronic health record (EHR) education intervention in large health care organization improves quality, efficiency, time, and impact on burnout. Medicine (Baltimore) 97(38):e12319. https://doi.org/10.1097/MD.0000000000012319.

Rumsfeld, D. 2011. Known and Unknown: A Memoir. Penguin.

Saeed, W., and C. Omlin. 2023. Explainable AI (XAI): A systematic meta survey of current challenges and future opportunities. Knowledge-Based Systems 263:110273. https://doi.org/10.1016/j.knosys.2023.110273.

Sahini, N., G. Stein, R. Zemmel, and D. M. Cutler. 2023. The potential impact of artificial intelligence on healthcare spending. Working Paper 30857. National Bureau of Economic Research. https://doi.org/10.3386/w30857.

Sallam, M. 2023. ChatGPT utility in health care education, research, and practice: Systematic review on the promising perspectives and valid concerns. Health Care (Basel) 11(6):887. https://doi.org/10.3390/healthcare11060887.

Salwei, M., and P. Carayon. 2022. A sociotechnical systems framework for the application of artificial intelligence in health care delivery. Journal of Cognitive Engineering and Decision Making 16(4):194–206. https://doi.org/10.1177/15553434221097357.

Sandalow, M. 2024. First into the Breach: ONC Final Rule Addressing AITransparency in Health Care. Bipartisan Policy Center. https://bipartisanpolicy.org/blog/first-into-the-breach-onc-final-rule-addressing-ai-transparency-in-health-care (accessed April 4, 2025).

Sanders, H. 2024. 260 McNuggets? McDonald’s ends AI drive-through tests amid errors. The New York Times. https://www.nytimes.com/2024/06/21/business/mcdonalds-ai-drive-thru-white-castle.html?smid=url-share (accessed April 4, 2025).

Sanderson, S. C., K. B. Brothers, N. D. Mercaldo, E. W. Clayton, A. H. Matheny Antommaria, S. A. Aufox, M. H. Brilliant, D. Campos, D. S. Carrell, J. Connolly, P. Conway, S. M. Fullerton, N. A. Garrison, C. R. Horowitz, G. P. Jarvik, D. Kaufman, T. E. Kitchner, R. Li, E. J. Ludman, C. A. McCarty, and I. A. Holm. 2017. Public attitudes toward consent and data sharing in biobank research:

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

A large multi-site experimental survey in the US. American Journal of Human Genetics 100(3):414–427. https://doi.org/10.1016/j.ajhg.2017.01.021.

Sandmann, S., S. Riepenhausen, L. Plagwitz, and J. Varghese. 2024. Systematic analysis of ChatGPT, Google search and Llama 2 for clinical decision support tasks. Nature Communications 15:2050. https://doi.org/10.1038/s41467-024-46411-8.

Sanyal, S. 2021. How much does artificial intelligence cost in 2021? Analytics Insight. https://www.analyticsinsight.net/artificial-intelligence/how-much-does-artificial-intelligence-cost-in-2021 (accessed April 4, 2025).

Sarasohn-Kahn, J. 2021. A Nutrition Label for Health IT. HEALTHPOPULI blog. https://www.healthpopuli.com/2021/09/08/a-nutrition-label-for-health-it (accessed April 4, 2025).

Saria, S. 2022. Not all AI is created equal: Strategies for safe and effective adoption. NEJM Catalyst Innovations in Care Delivery 3(2). https://doi.org/10.1056/CAT.22.0075.

Sartori, L., and A. Theodorou. 2022. A sociotechnical perspective for the future of AI: Narratives, inequalities, and human control. Ethics and Information Technology 24:4. https://doi.org/10.1007/s10676-022-09624-3.

Sauerbrei, A., A. Kerasidou, F. Lucivero, and N. Hallowell. 2023. The impact of artificial intelligence on the person-centred, doctor-patient relationship: Some problems and solutions. BMC Medical Informatics and Decision Making 23:73. https://doi.org/10.1186/s12911-023-02162-y.

Schmitt, L. 2022. Mapping global AI governance. AI and Ethics 2:303–314. https://doi.org/10.1007/s43681-021-00083-y.

Scott, I. A., S. M. Carter, and E. Coiera. 2021. Exploring stakeholder attitudes towards AI in clinical practice. BMJ Health & Care Informatics. 28:e100450. https://doi.org/10.1136/bmjhci-2021-100450.

Sellen, A., and E. Horvitz. 2024. The rise of the AI co-pilot: Lessons for design from aviation and beyond. Communications of the ACM 67(7):18–23. https://doi.org/10.1145/3637865.

Semprini, J. 2023. Examining racial disparities in unemployment among health care workers before, during, and after the COVID-19 pandemic. Journal of Patient-Centered Research and Reviews 10(3):136–141. https://doi.org/10.17294%2F2330-0698.2021.

Sendak, M. P., M. Gao, N. Brajer, and S. Balu. 2020. Presenting machine learning model information to clinical end users with model facts labels. NPJ Digital Medicine 3:41. https://doi.org/10.1038/s41746-020-0253-3.

Sendak, M. P., S. Balu, and W. N. Price ll. 2023. Enabling collaborative governance of medical AI. Nature Machine Intelligence 5:821–823. https://doi.org/10.1038/s42256-023-00699-1.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Sepasspour, R. 2023. A reality check and a way forward for the global governance of artificial intelligence. Bulletin of the Atomic Scientists 79(5):304–315. https://doi.org/10.1080/00963402.2023.2245249.

Shah, N. H., J. D. Halamka, S. Saria, M. Percina, T. Tazbaz, M. Tripathi, A. Callahan, H. Hildahl, and B. Anderson. 2024. A nationwide network of health AI assurance laboratories. JAMA 331(3):245–249. https://doi.org/10.1001/jama.2023.26930.

Shanafelt, T. D. 2021. Physician well-being 2.0: Where are we and where are we going? Mayo Clinic Proceedings 96(10):2682–2693. https://doi.org/10.1016/j.mayocp.2021.06.005.

Shearer, C. 2000. The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing 5:13–22.

Shickel, B., P. J. Tighe, A. Bihorac, and P. Rashidi. 2018. Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics 22(5):1589–1604. https://doi.org/10.1109/jbhi.2017.2767063.

Siegel, R., K. Gordon, and L. Dynan. 2021. Behavioral economics: A primer and applications to the UN Sustainable Development Goal of good health and well-being. EMBO Reports 4(2):16. https://doi.org/10.3390/reports4020016.

Silcox, C., E. Zimlichmann, K. Huber, N. Rowen, R. Saunders, M. McClellan, C. N. Kahn III, C. A. Salzberg, and D. W. Bates. 2024. The potential for artificial intelligence to transform healthcare: Perspectives from international health leaders. NPJ Digital Medicine 7:88. https://doi.org/10.1038/s41746-024-01097-6.

Siontis, G. C. M., R. Sweda, P. A. Noseworthy, P. A. Friedman, K. C. Siontis, and C. J. Patel. 2021. Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials. BMJ Health Care Informatics 28(1):e100466. https://doi.org/10.1136%2Fbmjhci-2021-100466.

Sjoding, M. W., R. P. Dickson, T. Iwashyna, S. E. Gay, and T. S. Valley. 2020. Racial bias in pulse oximetry measurement. New England Journal of Medicine 383:2477–2478. https://doi.org/10.1056/nejmc2029240.

Skovgaard, L. L., S. Wadmann, and K. Hoeyer. 2019. A review of attitudes towards the reuse of health data among people in the European Union: The primacy of purpose and the common good. Health Policy 123(6):564–571. https://doi.org/10.1016%2Fj.healthpol.2019.03.012.

Steerling, E., E. Siira, P. Nilsen, P. Svedberg, and J. Nygren. 2023. Implementing AI in health care—The relevance of trust: A scoping review. Frontiers in Health Services 3:1211150. https://doi.org/10.3389/frhs.2023.1211150.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Steimetz, E., J. Minkowitz, E. C. Gabutan, J. Ngichabe, H. Attia, M. Hershkop, F. Ozay, M. G. Hanna, and R. Gupta. 2024. Use of artificial intelligence chatbots in interpretation of pathology reports. JAMA Network Open 7(5):e2412767. https://doi.org/10.1001/jamanetworkopen.2024.12767.

Stern, A. D., A. Goldfarb, T. Minssen, and W. N. Price. 2022. AI insurance: How liability insurance can drive the responsible adoption of artificial intelligence in health care. NEJM Catalyst 3(4). http://dx.doi.org/10.1056/CAT.21.0242.

Subbaswamy, A., and S. Saria. 2020. From development to deployment: Dataset shift, causality, and shift-stable models in health AI. Biostatistics 21(1):345–352. https://doi.org/10.1093/biostatistics/kxz041.

Suresh, H., and J. V. Guttag. 2021. A framework for understanding unintended consequences of machine learning. In Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. New York: Association for Computing Machinery. https://doi.org/10.48550/arXiv.1901.10002.

Sutton, R., D. Pincock, D. C. Baumgart, D. C. Sadowski, R. N. Fedorak, and K. I. Kroeker. 2020. An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine 3:17. https://doi.org/10.1038%2Fs41746-020-0221-y.

Tabassi, E. 2023. Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1.

Taleb, N. N. 2007. The Black Swan: The Impact of the Highly Improbable. New York: Random House.

Tallberg, J., E. Erman, M. Furendal, J. Geith, M. Klamberg, and M. Lundgren. 2023. The global governance of artificial intelligence: Next steps for empirical and normative research. International Studies Review 25(3):viad040. https://doi.org/10.1093/isr/viad040.

Tang, H., and J. H. K. Ng. 2006. Googling for a diagnosis—use of Google as a diagnostic aid: Internet based study. BMJ 333(7579):1143–1145. https://doi.org/10.1136/bmj.39003.640567.AE.

Tao, W., A. N. Concepcion, M. Vianen, A. C. A. Marijnissen, F. P. G. J. Lafeber, T. R. D. J. Radstake, and A. Pandit. 2020. Multiomics and machine learning accurately predict clinical response to adalimumab and etanercept therapy in patients with rheumatoid arthritis. Arthritis & Rheumatology 73(2):212–222. https://doi.org/10.1002/art.41516.

Temple, S. W. P., and C. G. Rowbottom. 2024. Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

neck cancer patients. Applied Clinical Medical Physics 25(6):e14273. https://doi.org/10.1002/acm2.14273.

The Joint Commission. n.d. Responsible Use of Health Data Certification. https://www.jointcommission.org/what-we-offer/certification/certifications-by-setting/hospital-certifications/responsible-use-of-health-data-certification (accessed April 4, 2025).

The Light Collective. 2024. Collective AI Rights for Patients. https://lightcollective.org/wp-content/uploads/2024/06/Collective-AI-Rights-For-Patients-v_2.0.pdf (accessed April 4, 2025).

Thompson, C., T. Margo, F. B. Yu, and C. U. Lehmann. 2022. Developing a pediatric EHR testing & certification program in the USA: Initial phase. In MEDINFO 2021: One World, One Health—Global Partnership for Digital Innovation, Vol. 290. IOS Press, pp. 1020–1021. https://doi.org/10.3233/SHTI220247.

Tierney, A. A., G. Gayre, B. Hoberman, B. Mattern, M. Ballesca, P. Kipnis, V. Liu, and K. Lee. 2024. Ambient artificial intelligence scribes to alleviate the burden of clinical documentation. NEJM Catalyst Innovations in Care Delivery 5(3). https://doi.org/10.1056/CAT.23.0404.

Tighe, P., B. M. Gale, and S. E. Mossburg. 2024. Artificial intelligence and patient safety: Promise and challenges. PSNet. Agency for Healthcare Research and Quality. https://psnet.ahrq.gov/perspective/artificial-intelligence-and-patient-safety-promise-and-challenges (accessed April 4, 2025).

Topol, E. J. 2019. High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine 25:44–56. https://doi.org/10.1038/s41591-018-0300-7.

Tornero-Costa, R., A. Martinez-Millana, N. Azzopardi-Muscat, L. Lazeri, V. Traver, and D. Novillo-Ortiz. 2023. Methodological and quality flaws in the use of artificial intelligence in mental health research: Systematic review. JMIR Mental Health 10:e42045. https://doi.org/10.2196/42045.

Tran, B., A. Lenhart, R. Ross, and D. A. Dorr. 2019. Burnout and EHR use among academic primary care physicians with varied clinical workloads. AMIA Joint Summits on Translational Science Proceedings 2019:136–144.

Trout, K. E., L. W. Chen, F. A. Wilson, H. J. Tak, and D. Palm. 2022. The impact of electronic health records and meaningful use on inpatient quality. Journal for Healthcare Quality 44(2):e15–e23. https://doi.org/10.1097/JHQ.0000000000000314.

Tseng, P., R. Kaplan, B. Richman, M. Shah, and K. Schulman. 2018. Administrative costs associated with physician billing and insurance-related activities at an academic health care system. JAMA 319(7):691–697. https://doi.org/10.1001/jama.2017.19148.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

21st Century Cures Act. 2016. Public Law 114-255. https://www.congress.gov/114/plaws/publ255/PLAW-114publ255.pdf (accessed December 11, 2024).

U.S. Food and Drug Administration (FDA). 2021. Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan.

FDA. 2022. Artificial Intelligence and Machine Learning in Software as a Medical Device. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device (accessed April 4, 2025).

FDA. 2024. Artificial Intelligence & Medical Products: How CBER, CDER, CDRH, and OCP Are Working Together. https://www.fda.gov/media/177030/download?attachment (accessed July 3, 2024).

FDA. n.d. Using Intelligence & Machine Learning in the Development of Drug and Biological Products: Discussion Paper and Request for Feedback. https://www.fda.gov/media/167973/download?attachment;%C2%A0 https://www.fda.gov/media/165743/download?attachment;%20and%C2% (accessed July 3, 2024).

U.S. General Services Administration. n.d. AI Guide for Government: A Living and Evolving Guide to the Application of Artificial Intelligence for the US Federal Government. https://coe.gsa.gov/coe/ai-guide-for-government/understanding-managing-ai-lifecycle (accessed April 4, 2025).

Van den Bruel, A., T. Haj-Hassan, M. Thompson, F. Buntinx, and D. Mant. 2010. Diagnostic value of clinical features at presentation to identify serious infection in children in developed countries: A systematic review. Lancet 375(9717):834–845. https://doi.org/10.1016/s0140-6736(09)62000-6.

Van den Bruel, A., M. Thompson, F. Buntinx, and D. Mant. 2012. Clinicians’ gut feeling about serious infections in children: Observational study. BMJ 345:e6144. https://doi.org/10.1136/bmj.e6144.

Van der Heijden, A. A., M. D. Abramoff, F. Verbraak, M. V. van Hecke, A. Liem, and G. Nijpels. 2018. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. Acta Ophthalmology 96(1):63–68. https://doi.org/10.1111/aos.13613.

van Genderen, M. E., D. van de Sande, L. Hooft, A. A. Reis, A. D. Cornet, J. H. F. Oosterhoff, B. J. P. van der Ster, J. Huiskens, R. Townsend, J. van Bommel, D. Gommers, and J. van den Hoven. 2024. Charting a new course in healthcare: Early-stage AI algorithm registration to enhance trust and transparency. NPJ Digital Medicine 7(1):119. https://doi.org/10.1038%2Fs41746-024-01104-w.

Vanstone, M., C. Canfield, C. Evans, M. Leslie, M. A. Levasseur, M. MacNeil, M. Pahwa, J. Panday, P. Rowland, S. Taneja, L. Tripp, J. You, and J. Abelson. 2023. Towards conceptualizing patients as partners in health systems: A systematic

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

review and descriptive synthesis. Health Research Policy and Systems 21(1):12. https://doi.org/10.1186/s12961-022-00954-8.

Vasey, B., M. Nagendran, B. Campbell, D. A. Clifton, G. S. Collins, S. Denaxas, A. K. Denniston, L. Faes, B. Geerts, M. Ibrahim, X. Liu, B. A. Mateen, P. Mathur, M. D. McCradden, L. Morgan, J. Ordish, C. Rogers, S. Saria, D. S. W. Ting, P. Watkinson, W. Weber, P. Wheatstone, and P. McCulloch. 2022. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ 377:e070904. https://doi.org/10.1136/bmj-2022-070904.

Vijayakumar, A. 2023. Potential impact of artificial intelligence on the emerging world order. F1000Research 11:1186. https://doi.org/10.12688/f1000research.124906.2.

Vollmer, S., B. A. Mateen, G. Bohner, F. J. Király, R. Ghani, P. Jonsson, S. Cumbers, A. Jonas, K. S. L. McAllister, P. Myles, D. Granger, M. Birse, R. Branson, K. G. M. Moons, G. S. Collins, J. P. A. Ioannidis, C. Holmes, and H. Hemingway. 2020. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ 368:l6927. https://doi.org/10.1136/bmj.l6927.

Wallace, M. B., P. Sharma, P. Bhandari, J. East, G. Antonelli, R. Lorenzetti, M. Vieth, I. Speranza, M. Spadaccini, M. Desai, F. J. Lukens, G. Babameto, D. Batista, D. Singh, W. Palmer, F. Ramirez, R. Palmer, T. Lunsford, K. Ruff, E. Bird-Liebermann, V. Ciofoaia, S. Arndtz, D. Cangemi, K. Puddick, G. Derfus, A. S. Johal, M. Barawi, L. Longo, L. Moro, A. Repici, and C. Hassan. 2022. Impact of artificial intelligence on miss rate of colorectal neoplasia. Gastroenterology 163(1):295–304. https://doi.org/10.1053/j.gastro.2022.03.007.

Washington, V., K. DeSalvo, F. Mostashari, and D. Blumenthal. 2017. The HITECH era and the path forward. New England Journal of Medicine 377:904–906. https://doi.org/10.1056/NEJMp1703370.

Wiens, J., and E. S. Shenoy. 2018. Machine learning for healthcare: On the verge of a major shift in healthcare epidemiology. Clinical Infectious Diseases 66(1):149–153. https://doi.org/10.1093/cid/cix731.

Williams, J. 2024. Battle of the bots: As payers use AI to drive denials higher, providers fight back. Healthcare Financial Management Association. https://www.hfma.org/revenue-cycle/denials-management/health-systems-start-to-fight-back-against-ai-powered-robots-driving-denial-rates-higher (accessed March 7, 2025).

Wong, A., E. Otles, J. P. Donnelly, A. Krumm, J. McCullough, O. DeTroyer-Cooley, J. Pestrue, M. Phillips, J. Konye, C. Penoza, M. Ghous, and K. Singh. 2021. External validation of a widely implemented proprietary sepsis prediction

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

model in hospitalized patients. JAMA Internal Medicine 181(8):1065–1070. https://doi.org/10.1001/jamainternmed.2021.2626.

World Economic Forum. 2023. It’s Time We Embrace an Agile Approach to Regulating AI. https://www.weforum.org/agenda/2023/11/its-time-we-embrace-an-agile-approach-to-regulating-ai (accessed April 4, 2025).

World Economic Forum. 2024. AI Governance Alliance Briefing Paper Series. https://www.weforum.org/publications/ai-governance-alliance-briefing-paper-series (accessed April 4, 2025).

World Health Organization (WHO). 2016. Global Strategy on Human Resources for Health: Workforce 2030. https://iris.who.int/bitstream/handle/10665/250368/9789241511131-eng.pdf (accessed April 4, 2025).

WHO. 2021. Generating Evidence for Artificial Intelligence-Based Medical Devices: A Framework for Training, Validation and Evaluation. https://iris.who.int/bitstream/handle/10665/349093/9789240038462-eng.pdf?sequence=1 (accessed April 4, 2025).

Wu, D., S. Chen, Y. Zhang, H. Zhang, Q. Wang, J. Li, Y. Fu, S. Wang, H. Yang, H. Du, H. Zhu, H. Pan, and Z. Shen. 2021. Facial recognition intensity in disease diagnosis using automatic facial recognition. Journal of Personalized Medicine 11(11):1172. http://dx.doi.org/10.3390/jpm11111172.

Wu, K., E. Wu, B. Theodorou, W. Liang, C. Mack, L. Glass, J. Sun, and J. Zou. 2023. Characterizing the clinical adoption of medical AI devices through U. S. insurance claims. NEJM AI 1(1). https://doi.org/10.1056/AIoa2300030.

Wyatt, R., M. Laderman, L. Botwinick, K. Mate, and J. Whittington. 2016. Achieving Health Equity: A Guide for Health Care Organizations. IHI White Paper. Cambridge, MA: Institute for Healthcare Improvement.

Xing, Y., Y. Sun, H. Li, M. Tang, W. Huang, K. Zhang, D. Zhang, D. Zhang, and Q. Ma. 2018. CHA(2)DS(2)-VASc score as a predictor of long-term cardiac outcomes in elderly patients with or without atrial fibrillation. Clinical Interventions in Aging 13:497–504. https://doi.org/10.2147/CIA.S147916.

Yao, X., D. R. Rushlow, J. W. Inselman, R. G. McCoy, T. D. Thacher, E. M. Behnken, M. E. Bernard, S. L. Rosas, A. Akfaly, A. Misra, P. E. Molling, J. S. Krien, R. M. Foss, B. A. Barry, K. C. Siontis, S. Kapa, P. A. Pellikka, F. Lopez-Jimenez, Z. I. Attia, N. D. Shah, P. A. Friedman, and P. A. Noseworthy. 2021. Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: A pragmatic, randomized clinical trial. Nature Medicine 27:815–819. https://doi.org/10.1038/s41591-021-01335-4.

YOLE Group. 2024. AWS Steps Up AI Competition. https://www.yolegroup.com/strategy-insights/aws-steps-up-ai-competition (accessed April 4, 2025).

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.

Yordanova, M. Z. 2024. The applications of artificial intelligence in radiology: Opportunities and challenges. European Journal of Medical and Health Sciences 6(2):11–14. https://doi.org/10.24018/ejmed.2024.6.2.2085.

Zack, T., E. Lehman, M. Suzgun, J. A. Rodriguez, L. A. Celi, J. Gichoya, D. Jurafsky, P. Szolovits, D. W. Bates, R. E. Abdulnour, A. J. Butte, and E. Alsentzer. 2024. Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: A model evaluation study. Lancet Digital Health 6(1):E12–E22. https://doi.org/10.1016/s2589-7500(23)00225-x.

Zhu, C., P. K. Attaluri, P. J. Wirth, E. C. Shaffrey, J. B. Friedrich, and V. K. Rao. 2024. Current applications of artificial intelligence in billing practices and clinical plastic surgery. Plastic and Reconstructive Surgery—Global Open 12(7):e5939. https://doi.org/10.1097/GOX.0000000000005939.

Zink, A., M. E. Chernew, and H. T. Neprash. 2024. How should Medicare pay for artificial intelligence? JAMA Internal Medicine 184(8):863–864. https://doi.org/10.1001/jamainternmed.2024.1648.

Zong, J., and J. N. Matias. 2022. Bartleby: Procedural and substantive ethics in the design of research ethics systems. Social Media + Society 8(1). https://doi.org/10.1177/20563051221077021.

Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 133
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 134
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 135
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 136
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 137
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 138
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 139
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 140
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 141
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 142
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 143
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 144
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 145
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 146
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 147
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 148
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 149
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 150
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 151
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 152
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 153
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 154
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 155
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 156
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 157
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 158
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 159
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 160
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 161
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 162
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 163
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 164
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 165
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 166
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 167
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 168
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 169
Suggested Citation: "References." National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. Washington, DC: The National Academies Press. doi: 10.17226/29087.
Page 170
Next Chapter: Author Information
Subscribe to Email from the National Academies
Keep up with all of the activities, publications, and events by subscribing to free updates by email.