About
Toward a Code of Conduct Framework for Artificial Intelligence in Health, Health Care, and Biomedical Science
The Artificial Intelligence Code of Conduct (AICC) project is a pivotal initiative of the NAM, aimed at providing a guiding framework to ensure that AI algorithms and their application in health, health care, and biomedical science perform accurately, safely, reliably, and ethically in the service of better health for all.
In May 2025, the NAM released the special publication, An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action, which presents a unifying AI Code of Conduct framework developed to align the field around responsible development and application of AI and to catalyze collective action to ensure that the transformative potential of AI in health and medicine is realized.
AICC Framework
The NAM AI Code of Conduct provides a unifying framework to guide responsible, equitable, and human-centered AI. The AICC Code Commitments serve as core values to guide organizations in how they develop, purchase, or use AI. They help promote inclusion, alignment, and trust while reducing confusion and inconsistency across the field. The Code provides:
- A shared compass that helps align different users across the field
- A reference standard that organizations can use to assess and align their own internal policies
- A bridge between high-level principles and on-the-ground action
Code Commitments
Advance Humanity
- Development of standards and other governance structures to assess alignment by developers and users of health AI with societal and cultural goals for health AI
- Incentives and structures for independent evaluation, certification to the AI Code Commitments, and public and transparent reporting on certification status
Ensure Equity
- Standardized metrics to assess and report bias in data, AI output, and AI use, in the interest of equitable distribution of benefit and risk
- Incentives and support to low-resourced organizations and communities to ensure equitable access to the benefits of AI
Engage Impacted Individuals
- Participation by all key stakeholders across the health AI lifecycle
- Local governance bodies, which includes all stakeholders in the AI lifecycle cross-purposes
- Common understanding and education of all affected parties
Improve Workforce Well-Being
- Positive work and learning environments and culture
- Measurement, assessment, strategies, and research
- Reskilling and training programs for workforce AI competency
- Disruptive technologies with change management strategies that promote worker well-being
Monitor Performance
- Standardized quality and safety metrics to be used to assess the impact of the use of health AI on health outcomes
- Aligned frameworks for safety, equity, and quality in AI performance
Innovate and Learn
- A well supported national health AI research agenda
- Participation in shared learning across all stakeholders
- Innovation as a core investment
Code Principles
Building on the NAM Learning Health System initiative, the Code Principles outline the key values and norms for governing health AI, promoting trust, maximizing benefits, and reducing risks across health, health care, and biomedical science.
Engaged
Center AI development on people’s needs, preferences, and goals throughout its lifecycle.
Safe
Maintain strict oversight to prevent and address any potential harm from AI in health and medicine.
Effective
Ensure AI consistently improves health and well-being while upholding ethical standards.
Equitable
Demonstrate fairness in AI design, access, and outcomes, minimizing bias and risk.
Efficient
Use AI to improve health outcomes while reducing resource use and environmental impact.
Accessible
Make every stage of AI development and governance open and inclusive for all stakeholders.
Transparent
Share clear, understandable information about how AI works, performs, and impacts outcomes.
Accountable
Document actions, benefits, and safeguards to ensure responsibility for AI’s effects.
Secure
Protect health data through validated privacy and security measures that enable safe, continuous learning.
Adaptive
Continuously monitor and refine AI systems to drive ongoing learning and improvement in health and science.
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Frequently Asked Questions
What is the vision of AICC?
The AICC vision is to align and catalyze collective action to realize the potential of AI to revolutionize health care delivery, generate groundbreaking advances in health research, and contribute to robust health for all, adhering to the highest standards of ethics, equity, privacy, security, and accountability.
What are the key activities to achieve the project vision?
The AICC activities are to: 1) harmonize the many sets of AI principles/frameworks/blueprints for health care and biomedical science and identify and fill the gaps to create a best practice AI Code of Conduct with “guideline interoperability”; 2) align the field in advancing broad adoption and embedding of the harmonized AI Code of Conduct; 3) identify the roles and responsibilities of each stakeholder at each stage of the AI lifecycle; 4) describe the national architecture needed to support responsible health care AI; and 5) identify ways to increase the speed of learning about how to govern health care AI in service of a learning health system.
What does it mean to “align the field” and/or “embed the Code of Conduct”?
Aligning the field means bringing together a broad stakeholder group to ensure all perspectives are understood throughout the process of developing the Code of Conduct to: 1) assure, that to the extent possible, the Code reflects the needs and priorities of all parties; and 2) earn stakeholder support for and commitment to the Code, with the ultimate goal of having the Code woven throughout the fabric of health, health care, and biomedical science.
What is included in the “systems view” of AI?
The systems view of AI in health care and biomedical science considers the aspects of the AI lifecycle (e.g., data acquisition, validation, and representativeness, data modeling and testing, systems procurement and implementation, post-implementation vigilance, etc.), the stakeholders’ roles in each phase, and identifies the ecosystem needed to support trustworthy AI for health, health care, and biomedical science.
Does the project include both predictive AI and Large Language Model (LLM) AI?
This project will address both predictive AI (e.g., models that help to identify patients who are at risk of developing certain conditions, recommending treatment plans, and predicting outcomes) as well as LLM AI (e.g., ChatGPT, Bard, OPT-IML, etc.), as both have significant implications for health, health care, and biomedical science and are becoming integrated in their use.
What are the outputs of the project?
The AICC outputs are: 1) a harmonized and broadly supported Code of Conduct; 2) a comprehensive landscape assessment that includes a systematic review of the literature; a review of the guidelines/frameworks/blueprints from federal agencies; and the guidelines issued by medical specialty societies; 3) a description of the roles and responsibilities of each stakeholder at each stage of the AI lifecycle; 4) a description of the national architecture needed to support responsible health care AI; and 5) identification of priority actions going forward. The work products that contain the above include: 1) summaries from the AICC Steering Committee meetings; 2) a NAM Commentary paper outlining the landscape assessment and key components of the Code; and 3) a final NAM special publication containing a proposed Code of Conduct framework to be deployed for testing, validation, learning, and improvement.
How long will the project last?
The current AICC project will last for 3 years and will conclude in December 2025. We anticipate that additional projects may spawn from this work.
Is this for the U.S. only?
The work will draw primarily from the U.S. experience but will be informed by international efforts to ensure the inclusion of best practices, and to support global AI guideline harmonization efforts for health, health care, and biomedical science. The stakeholder groups involved in the AICC project and developing the Code include patients and families; providers; privacy, security and ethics experts; equity experts; health systems; tech companies, government agencies; biomedical scientists and researchers; health plans; pharma and health care product manufacturers; professional societies; medical societies; and health care AI collaborations. In addition, the NAM will work with these stakeholder groups to embed the Code into their own sets of guidelines and principles and develop their specific implementation guides. The project will be guided by a Steering Committee of national thought leaders representing all key stakeholders.
What is the role of the AICC Steering Committee?
The AICC Steering Committee provides strategic guidance for the project as a whole and overarching leadership for the development of a Code of Conduct fully informed by stakeholders, assuring that process and outcomes warrant and achieve the desired broad support for and implementation of the Code. The project team identified candidates for steering committee membership to ensure broad stakeholder engagement and includes individuals with expertise in ethics, patient advocacy, communications, health systems, technology, and research. The criteria for selection also included: 1) national recognized thought leadership; 2) capacity to influence the adoption and embedding of the AI Code of Conduct through the industry; and 3) ability to provide thought leadership and strategic guidance on issues such as governance, policy development, environmental awareness, and risk analysis.
How does the AICC align efforts and synergistically reinforce other initiatives in the field?
A foundational principle in the development of the AICC project was the importance of honoring and building upon the work that has already been done to promote trustworthy AI in health, health care, and biomedical science. To that end, an early activity in the project plan is a comprehensive landscape assessment that includes a systematic review of the literature; a review of the guidelines/frameworks/blueprints from federal agencies; and the guidelines issued by medical specialty societies. This environmental scan is serving as the foundation upon which the Code is being developed. However, the AICC will not provide the specific implementation guidance on topics already covered by federal agencies or other coalitions, such as those presented in the NIST Risk Management Framework or the Blueprint for Trustworthy AI in Healthcare produced by the Coalition for Health AI (CHAI). Throughout the course of the project, the NAM effort will inform the efforts of CHAI, which is providing robust best practice technical guidance, including assurance labs and implementation guides to enable clinical systems to apply the Code of Conduct. Similarly, the efforts of the CHAI and other groups addressing responsible AI will inform and clarify areas that will need to be addressed in the NAM Code of Conduct. The work and final deliverables of these projects are mutually reinforcing and coordinated to ensure broad adoption and active implementation of the AICC in the field.
How does the AICC framework relate to the LHS Shared Commitments Initiative?
The NAM Learning Health System Shared Commitments provided the foundation for the Code Principles, seamlessly integrating the constructs. Just as the LHS Shared Commitments establish expectations for all participants in the health system, the Code Principles provide analogous guidance specifically tailored to the development and implementation of health AI. They represent a natural evolution of the LHS framework to address emerging technologies. The Code Principles reflect the values and norms to be applied in the context of health AI governance to promote trust while ensuring the benefits and mitigating the risks associated with Al in health, health care, and biomedical science.
Contact Information
Interested in joining?
This project is supported by The Gordon and Betty Moore Foundation, The Patrick J. McGovern Foundation, The California Health Care Foundation, Epic, and the National Institutes of Health.
For more information on the project, please email Laura Adams, NAM Senior Advisor, at [email protected] and Sunita Krishnan, Program Officer, at [email protected].
