A Nationwide Network of Health AI Assurance Laboratories
Abstract
Importance Given 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. Observations While 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 relevance The 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.
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This call has spurred researchers at academic medical centers, including Stanford, to propose a nationwide network of health AI assurance laboratories to develop a testing and evaluation (T&E) framework as well as corresponding processes and policies.
“This call has spurred researchers at academic medical centers, including Stanford, to propose a nationwide network of health AI assurance laboratories to develop a testing and evaluation (T&E) framework as well as corresponding processes and policies 5 .”
From Standing on FURM Ground: A Framework for Evaluating Fair, Useful, and Reliable AI Models in Health Care Systems (2024) · cites this paper as [5]
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References 23 · Cited by 114
- Authors
- Shah, N. H., Halamka, J. D., Saria, S., Pencina, M., Tazbaz, T., Tripathi, M., Callahan, A., Hildahl, H., & Anderson, B.
- Affiliations
- Stanford Medicine (US)
- Mayo Clinic (US)
- Johns Hopkins University (US)
- Duke University (US)
- United States Food and Drug Administration (US)
- Office of the National Coordinator for Health Information Technology (US)
- Mitre (United States) (US)
- Corresponding author
- Shah, Nigam H.
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2024-01-16
- Volume
- 331
- Issue
- 3
- Pages
- 245
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W4389992596
- Cited by
- 114
- References
- 23
- Citations by year
- 2026: 23 · 2025: 42 · 2024: 49
- FWCI
- 4.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- health AI, artificial intelligence in healthcare, AI model evaluation, public-private partnerships, AI lifecycle management, AI safety, algorithmic fairness, clinical implementation of AI, performance reporting
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Health Facilities, Laboratories, Quality Assurance, Health Care, Quality of Health Care, United States, Public-Private Sector Partnerships
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