Key collaborators across the healthcare AI life cycle now have a common set of principles to which they can hold each other. And that means everyone from developers and researchers to providers, regulators and even patients.

The group defining the code of conduct, an AI steering committee of the National Academy of Medicine (NAM), says it hopes the guidance will

provide touchstones around which health AI governance—facilitative and precautionary—can be shaped, tested, validated and continually improved as technology, governance capability and insights advance.

NAM senior advisor Laura Adams and colleagues present the organization’s thinking in a draft posted April 8. The team’s recommended code-of-conduct principles, 10 in number, urge healthcare AI stakeholders to help make sure the technology is unfailingly:

1. Engaged: ‘Understanding, expressing, and prioritizing the needs, preferences, goals of people, and the related implications throughout the AI life cycle.’
    
2. Safe: ‘Attendance to and continuous vigilance for potentially harmful consequences from the application of AI in health and medicine for individuals and population groups.’
    
3. Effective: ‘Application proven to achieve the intended improvement in personal health and the human condition, in the context of established ethical principles.’
    
4. Equitable: ‘Application accompanied by proof of appropriate steps to ensure fair and unbiased development and access to AI-associated benefits and risk mitigation measures.’
    
5. Efficient: ‘Development and use of AI associated with reduced costs for health gained, in addition to a reduction, or at least neutral state, of adverse impacts on the natural environment.’
    
6. Accessible: ‘Ensuring that seamless stakeholder access and engagement is a core feature of each phase of the AI life cycle and governance.’
    
7. Transparent: ‘Provision of open, accessible, and understandable information on component AI elements, performance, and their associated outcomes.’
    
8. Accountable: ‘Identifiable and measurable actions taken in the development and use of AI, with clear documentation of benefits, and clear accountability for potentially adverse consequences.’
    
9. Secure: ‘Validated procedures to ensure privacy and security, as health data sources are better positioned as a fully protected core utility for the common good, including use of AI for continuous learning and improvement.’
    
10.  Adaptive: ‘Assurance that the accountability framework will deliver ongoing information on the results of AI application, for use as required for continuous learning and improvement in health, healthcare, biomedical science and, ultimately, the human condition.’

In addition, the draft offers a set of six proposed commitments stakeholders could take to “broadly direct the application and evaluation of the code principles in practice.” The commitments:  

1. Focus. Protect and advance human health and human connection as the primary aims.
2. Benefits. Ensure equitable distribution of benefit and risk for all.
3. Involvement. Engage people as partners with agency in every stage of the life cycle.
4. Workforce well-being. Renew the moral well-being and sense of shared purpose to the healthcare workforce.
5. Monitoring. Monitor and openly and comprehensibly share methods and evidence of AI’s performance and impact on health and safety.
6. Innovation. Innovate, adopt, collaboratively learn, continuously improve and advance the standard of clinical practice.

NAM suggests its 10 principles and six commitments “reflect simple guideposts to guide and gauge behavior in a complex system and provide a starting point for real-time decision making and detailed implementation plans to promote the responsible use of AI.” More:

Engagement of all key stakeholders in the co-creation of this Code of Conduct framework is essential to ensure the intentional design of the future of AI-enabled health, healthcare and biomedical science that advances the vision of health and well-being for all.

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