A Federated Registration System for Artificial Intelligence in Health
Abstract
This Viewpoint discusses a suggested framework of local registries to record and track all health artificial intelligence technologies used in clinical care, with the goal of providing transparency on these technologies and helping speed adoption while also protecting patient well-being.
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The evaluation of their capacity for ongoing improvement is based on the system's ability to incorporate model updates and integrate feedback loops.
“The evaluation of their capacity for ongoing improvement is based on the system’s ability to incorporate model updates and integrate feedback loops.122,123”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [123]
Cites
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Darzi, A., Holmes, C., Yau, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1037-7 · Cited by 748 · see citing works
External validation of AI models in health should be replaced with recurring local validation
Youssef, A., Pencina, M., Thakur, A., Zhu, T., Clifton, D., & Shah, N. H. (2023) · Nature Medicine · doi:10.1038/s41591-023-02540-z · Cited by 170 · see citing works
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A foundational architecture for AI agents in healthcare
Liu, F., Niu, Y., Zhang, Q., Wang, K., Dong, Z., Wong, I. N., Cheng, L., Li, T., Duan, L., Li, K., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102374 · Cited by 41 · see citing works
“122,123 AI systems that embed continuous learning processes are better equipped to adapt to emerging disease patterns, evolving treatment strategies, and changing patient outcomes.”
- Authors
- Pencina, M. J., McCall, J., & Economou-Zavlanos, N. J.
- Affiliations
- Duke University (US)
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2024-09-10
- Volume
- 332
- Issue
- 10
- Pages
- 789
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W4401511417
- Cited by
- 15
- References
- 2
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 3
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R61NS120246)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- artificial intelligence in healthcare, clinical implementation of AI, patient safety, health data privacy, clinical care
- MeSH terms
- Risk Evaluation and Mitigation, Digital Health, Artificial Intelligence, Humans, Registries, United States, Practice Guidelines as Topic, Federal Government
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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