POLICY BRIEF article
Front. Syst. Biol.
Sec. Systems Concepts, Theory and Policy in Biology and Medicine
Streamlining IRB Review of AI Human Subjects Research (AIHSR): The Three-Stage Framework
Tamiko Eto 1,2
Heather Miller 3
Mark Lifson 4
David Vidal 4
1. Center for AI & Digital Policy (CAIDP), Washington, DC, United States
2. TechInHSR, Madison, United States
3. Northstar Review Board, seatlle, United States
4. Mayo Clinic Minnesota, Rochester, United States
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Abstract
Oversight of AI in Human Subjects Research (AI HSR) presents unique challenges. These challenges arise from both the non-linear and iterative nature of AI development, as well as from the way AI shifts risk from individual research subjects to larger populations affected by AI-driven decisions and data handling. Traditional IRBs struggle to keep pace with these changes, which can lead to gaps in risk assessment and delays in the review process. There is a growing need for transparent, repeatable methods to manage AI risk in healthcare. This paper introduces a risk-based oversight model aligning ethical and regulatory review with an AI project's stage of maturity and potential human impact. The framework supports appropriate regulatory pathways and documents expectations while maintaining effective protection of human subjects.
Summary
Keywords
AIgovernance, artificial intelligence, Human Research Protections, IRB review, Practical Frameworks, research compliance, research ethics
Received
04 February 2026
Accepted
20 February 2026
Copyright
© 2026 Eto, Miller, Lifson and Vidal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Tamiko Eto
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.