THE LEARNING HEALTH SYSTEM SERIES

An Artificial Intelligence Code of Conduct for
Health and Medicine

Essential Guidance for Aligned Action

Laura Adams, Elaine Fontaine,
Michael Matheny, Sunita Krishnan, Editors

WASHINGTON, DC
NAM.EDU

NATIONAL ACADEMIES PRESS 500 Fifth Street, NW Washington, DC 20001

This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM signifies that it is the product of a carefully considered process and is a contribution worthy of public attention but does not constitute an endorsement of conclusions and recommendations by the NAM. The views presented in this publication are those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine.

This publication was completed with support from the Gordon and Betty Moore Foundation, the California Health Care Foundation, the Patrick J. McGovern Foundation, and the National Institutes of Health (Contract Number HHSN263201800029I). Any opinions, findings, or conclusions expressed in this publication do not necessarily reflect the views of any organization or agency that provided support for this publication.

International Standard Book Number-13: 978-0-309-09895-3
Digital Object Identifier: https://doi.org/10.17226/29087

Copyright 2025 by the National Academy of Sciences. National Academies of Sciences, Engineering, and Medicine and National Academies Press and the graphical logos for each are all trademarks of the National Academy of Sciences. All rights reserved.

Printed in the United States of America.

Suggested citation: National Academy of Medicine. 2025. An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action. L. Adams, E. Fontaine, M. Matheny, and S. Krishnan, editors. NAM Special Publication. Washington, DC: National Academies Press. https://doi.org/10.17226/29087.

“Knowing is not enough; we must apply.
Willing is not enough; we must do.”
—GOETHE

ABOUT THE NATIONAL ACADEMY OF MEDICINE

The National Academy of Medicine is one of three Academies constituting the National Academies of Sciences, Engineering, and Medicine (the National Academies). The National Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. The National Academies also encourage education and research, recognize outstanding contributions to knowledge, and increase public understanding in matters of science, engineering, and medicine.

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The National Academy of Medicine (formerly the Institute of Medicine) was established in 1970 under the charter of the National Academy of Sciences to advise the nation on issues of health, health care, and biomedical science and technology. Members are elected by their peers for distinguished contributions to medicine and health. Dr. Victor J. Dzau is president.

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AUTHOR GROUP

ANDREW BINDMAN, Kaiser Permanente

GRACE CORDOVANO, Enlightening Results

JODI DANIEL, Wilson Sonsini Goodrich & Rosati

WYATT DECKER, UnitedHealth Group

PETER J. EMBÍ, Vanderbilt University Medical Center

GIANRICO FARRUGIA, Mayo Clinic (Author Group Co-Lead)

KADIJA FERRYMAN, Johns Hopkins University

SANJAY GUPTA, Emory University

ERIC HORVITZ, Microsoft

ROY JAKOBS, Royal Philips (Author Group Co-Lead)

KEVIN B. JOHNSON, University of Pennsylvania

PETER LEE, Microsoft

KENNETH MANDL, Harvard University

KEDAR MATE, Institute for Healthcare Improvement

DEVEN MCGRAW, Citizen Health

BAKUL PATEL, Google (Author Group Co-Lead)

PHILIP PAYNE, Washington University

VARDIT RAVITSKY, The Hastings Center

SUCHI SARIA, Bayesian Health and Johns Hopkins University

ERIC TOPOL, Scripps Research

SELWYN VICKERS, Memorial Sloan Kettering Cancer Center

Development of this publication was facilitated by the contributions of the following people:

Editors:

LAURA ADAMS, National Academy of Medicine

ELAINE FONTAINE, National Academy of Medicine

SUNITA KRISHNAN, National Academy of Medicine

MICHAEL MATHENY, Vanderbilt University Medical Center

Contributors:

DAVID DORR, Oregon Health & Science University

ANDREA DOWNING, Light Collective

TYLER LOFTUS, University of Florida

SHAUNA OVERGAARD, Mayo Clinic

RAVI B. PARIKH, Emory University

Staff

Development of this publication was facilitated by contributions of the following NAM staff, under the guidance of J. Michael McGinnis, Leonard D. Schaeffer Executive Officer and Executive Director of the NAM Leadership Consortium:

LAURA ADAMS, Senior Advisor

AUDREY ELLIOTT, Associate Program Officer

SUNITA KRISHNAN, Senior Program Officer

ANNIE MURFF, Senior Program Assistant

DISCLOSURE OF CONFLICTS OF INTEREST

Authors, editors, and contributors to this publication have filed disclosure statements on perceived or actual interests related to the development and application of artificial intelligence in health, health care, and biomedical science. These statements cover the following circumstances:

1. ORGANIZATIONAL AFFILIATIONS. Business relationships (as an employee, owner, officer, director, consultant, etc.) and remunerated or volunteer non-business relationships (e.g., professional organizations, trade associations, public interest or civic groups) that might be relevant to this National Academy of Medicine (NAM) activity.
2. FINANCIAL INTERESTS. Significant financial interests (research support, investments, intellectual property, commercial holdings) that might be perceived as relevant to this NAM activity.
3. ASSOCIATES. Significant interest known to you as part of close associates or friends whose welfare might be viewed as relevant to this NAM activity.
4. FAMILY MEMBERS. Significant interests of family members whose welfare might be viewed as relevant to this NAM activity.

A summary of relevant items is included with the author information starting.

See Author Information.

See Editor Information.

See Contributor Information.

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ACKNOWLEDGMENTS

The following individuals participated in expert working groups to consider and characterize real-world activities and stakeholder perspectives, responsibilities, and needs in the context of the Artificial Intelligence Code of Conduct.

PAT BAIRD, Philips

SURESH BALU, Duke University

LISA BARI, Innovaccer

VALERIE BONHAM, Kennedy Krieger Institute

ANASTASIA CHRISTIANSON, Pfizer

THERESA CULLEN, Pima County Health Department

MOHAMMAD HOSSEINI, Northwestern University

TIMOTHY HSU, Association for the Advancement of Medical Instrumentation

ABEL KHO, Northwestern Medicine

DAVID MARC, Community Clinical Services

GENEVIEVE MELTON-MEAUX, University of Minnesota

STEVE MIFF, Parkland Center for Clinical Innovation

RENE QUASHIE, Consumer Technology Association

MARK SENDAK, Duke University

KAVEH SHOJANIA, University of Toronto

WILL SHRANK, Andreessen Horowitz

LAUREN SILVIS, Tempus

KARANDEEP SINGH, University of California, San Diego

MARINA SIROTA, University of California, San Francisco

BOB WACHTER, University of California, San Francisco

NICOLE WEISKOPF, Oregon Health & Science University

SHANNON WEST, Datavant

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REVIEWERS

This Special Publication was reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and critical comments that will assist the National Academy of Medicine in making its published Special Publication as sound as possible and to ensure that the Special Publication meets the institutional standards for quality, objectivity, and evidence. The review comments and draft manuscript remain confidential to protect the integrity of the deliberative process.

We wish to thank the following individuals for their contributions:

The reviewers listed above provided many constructive comments and suggestions, but they were not asked to endorse the content of the Special Publication and did not see the final draft before it was published.

Review of this publication was overseen by PAUL C. TANG, Stanford University School of Medicine. Responsibility for the final content rests entirely with the author group and the National Academy of Medicine.

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CONTENTS

Preface

Acronyms and Abbreviations

Executive Summary

1 Introduction

2 Background

3 AI Code of Conduct Framework

4 Health and Health Care AI Lifecycle

5 Translation of the AI Code of Conduct Framework to Real-World Applications

6 The Components Required to Advance Responsible AI

7 Advancing the AI Code of Conduct Framework: The “Vital Few” Priority Actions

8 Conclusion

References

Author Information

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BOXES, FIGURES, AND TABLES

BOXES

2-1 Applying Lessons from EHR Adoption to Health AI Implementation

2-2 The Vision of the Learning Health System

FIGURES

ES-1 Representation of the Tight-Loose-Tight model of leading change at scale adapted for the AI in health and health care context

2-1 High-level categories of AI

2-2 Conceptualization of human centeredness emphasizing human agency and connections

2-3 Continuum of the forms of governance from most conceptual to most enforceable

2-4 Application of AI throughout the Learning Health System cycle

4-1 Some key differences between AI lifecycle development and standard application development

6-1 Representation of the Tight-Loose-Tight model of leading change at scale adapted for the AI in health and health care context

TABLES

ES-1 Distinct Contributions of Various Stakeholders to the Application of the AI Code of Conduct Framework

ES-2 Summary Priority Actions to Operationalize the AICC Code Commitments

2-1 Comparative Features and Implications of Various Categories of AI

2-2 A Sampling of Key International AI Governance Efforts Since 2019

3-1 Crosswalk of Draft AICC Code Principles and Commitments

3-2 Updated AICC Code Principles

3-3 Updated AICC Code Commitments

4-1 A Comparison of Relevant AI Lifecycles with Stage and Content Alignment

4-2 Description of the AI Development Lifecycle

5-1 Description of Key Stakeholder Groups

5-2 Common Themes for Action Among Expert Working Groups

5-3 Distinct Contributions of Various Stakeholders to the Application of the AI Code of Conduct Framework

6-1 Summary Key Components to Advance Health AI via the Tight-Loose-Tight Framework

7-1 Summary Priority Actions to Operationalize the AICC Code Commitments

PREFACE

Recent advancements in artificial intelligence (AI) technologies have unlocked unprecedented opportunities in health, health care, and biomedical science, and these breakthroughs hold the potential to fundamentally transform approaches to medical and health research, health promotion, disease prevention, diagnosis, treatment, and health system management. By enabling more effective, efficient, and personalized care, AI stands poised to address some of the most persistent challenges in the health sector, provided it is properly governed and effectively stewarded. As the health sector grapples with converging challenges, AI has the potential to be a much-needed transformative force, capable of helping solve some of the most intractable issues in health care today. These issues—including inequities in access, rising costs, clinician burnout, and the growing burden of chronic disease—demand bold, new approaches. The promise of AI extends beyond its technological capabilities to encompass a more profound reconsideration of care delivery, aiming to improve outcomes for everyone, particularly our most vulnerable.

However, the development and deployment of AI introduces critical ethical, accountability, and safety considerations. As AI technologies diffuse into health care, it is vital to establish robust guidance for their integration, ensuring alignment with the foundational commitment to improve health and well-being for all.

This National Academy of Medicine (NAM) Special Publication, An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action, addresses this imperative. By harmonizing existing AI principles, identifying gaps, and aligning them with the core commitments of the NAM’s Learning Health System (LHS), it provides a comprehensive, adaptable set of guidelines for health care organizations and stakeholders. These principles are intended as guideposts for the development, implementation, and continuous improvement of AI systems, ensuring they uphold the highest standards of integrity, safety, and effectiveness.

As AI becomes more embedded in health care, it is essential to ensure that its use fosters trust and collaboration across the health care ecosystem. In the spirit of the NAM’s Shared Commitments that set out expectations for all participants and stakeholders in health and health care, the Code Commitments presented in this publication offer a touchstone for organizations as they develop their AI strategies and approaches. By fostering collaboration and shared understanding, the Code of Conduct aims to minimize the risks of fragmented efforts and maximize the collective impact of AI in improving health care.

At the heart of this work is the belief that AI has the potential to play a pivotal role in creating a more effective, accessible, and sustainable health care system. This publication not only explores the role of this technology in advancing human health but also underscores the importance of ensuring that its benefits reach all individuals, creating a future where excellence in health care is a reality for everyone.

ACRONYMS AND ABBREVIATIONS