Currently submitted to: JMIR AI
Date Submitted: May 31, 2026
Open Peer Review Period: Jun 2, 2026 - Jul 28, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Four Imperatives for Health Systems Governing Racial Bias in Healthcare AI
ABSTRACT
Racial and ethnic disparities in the United States healthcare are not new. What is new is the speed at which artificial intelligence (AI) is being deployed across clinical and administrative health system functions, and the documented risk that these technologies will encode, amplify, and scale existing inequities. This Viewpoint argues that health system leaders are the most consequential actors in determining whether AI narrows or widens racial health disparities, and advances four operational imperatives for responsible governance: govern before you deploy, demand transparency from vendors, build equity into clinical workflows, and make disparities visible. These imperatives complement existing frameworks, including the CHAI Blueprint, the NIST AI Risk Management Framework, and the Rajkomar et al. health equity framework, and are informed by the emerging regulatory landscape governing AI in healthcare. The argument is not against AI. It is for AI governance worthy of the trust being placed in it.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.