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Health Care Artificial Intelligence Code of Conduct

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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 
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