Artikel ; Online: A Nationwide Network of Health AI Assurance Laboratories.
2023 Band 331, Heft 3, Seite(n) 245–249
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, ... ...
| 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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| Mesh-Begriff(e) | Artificial Intelligence/standards ; Health Facilities/standards ; Laboratories/standards ; Public-Private Sector Partnerships ; Quality Assurance, Health Care/standards ; Delivery of Health Care/standards ; Quality of Health Care/standards ; United States | |||||
| Sprache | Englisch | |||||
| Erscheinungsdatum | 2023-12-20 | |||||
| Erscheinungsland | United States | |||||
| Dokumenttyp | Journal Article | |||||
| ZDB-ID | 2958-0 | |||||
| ISSN | 1538-3598 ; 0254-9077 ; 0002-9955 ; 0098-7484 | |||||
| ISSN (online) | 1538-3598 | |||||
| ISSN | 0254-9077 ; 0002-9955 ; 0098-7484 | |||||
| DOI | 10.1001/jama.2023.26930 | |||||
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| Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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