Cited by 114Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

A Nationwide Network of Health AI Assurance Laboratories

Shah, Nigam H., Halamka, John D., Saria, Suchi, Pencina, Michael, Tazbaz, Troy, Tripathi, Micky, Callahan, Alison, Hildahl, Hailey +1 more·2024·JAMA

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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Crossref · 10.1001/jama.2023.26930

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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 .”

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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
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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