A Nationwide Network of Health AI Assurance Laboratories

Author:

Shah Nigam H.12,Halamka John D.23,Saria Suchi2456,Pencina Michael27,Tazbaz Troy8,Tripathi Micky9,Callahan Alison1,Hildahl Hailey3,Anderson Brian210

Affiliation:

1. Stanford Medicine, Palo Alto, California

2. Coalition for Health AI, Dover, Delaware

3. Mayo Clinic Platform, Mayo Clinic, Rochester, Minnesota

4. Bayesian Health, New York, New York

5. Johns Hopkins University, Baltimore, Maryland

6. Johns Hopkins Medicine, Baltimore, Maryland

7. Duke AI Health, Duke University School of Medicine, Durham, North Carolina

8. US Food and Drug Administration, Silver Spring, Maryland

9. US Office of the National Coordinator for Health IT, Washington, DC

10. MITRE Corporation, Bedford, Massachusetts

Abstract

ImportanceGiven the importance of rigorous development and evaluation standards needed of artificial intelligence (AI) models used in health care, nationwide accepted procedures to provide assurance that the use of AI is fair, appropriate, valid, effective, and safe are urgently needed.ObservationsWhile there are several efforts to develop standards and best practices to evaluate AI, there is a gap between having such guidance and the application of such guidance to both existing and new AI models being developed. As of now, there is no publicly available, nationwide mechanism that enables objective evaluation and ongoing assessment of the consequences of using health AI models in clinical care settings.Conclusion and RelevanceThe need to create a public-private partnership to support a nationwide health AI assurance labs network is outlined here. In this network, community best practices could be applied for testing health AI models to produce reports on their performance that can be widely shared for managing the lifecycle of AI models over time and across populations and sites where these models are deployed.

Publisher

American Medical Association (AMA)

Subject

General Medicine

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