A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare

Health systems face the challenge of balancing innovation and safety to responsibly implement artificial intelligence (AI) solutions. The rapid proliferation, growing complexity, ethical considerations, and rising demand for these tools require timely and efficient processes for rigorous evaluation and ongoing monitoring. Current AI evaluation frameworks often lack the practical guidance for health systems to address these challenges. To fill this gap, we developed a prescriptive evaluation framework informed by a literature review, in-depth interviews with key stakeholders, including patients, and a multidisciplinary design workshop. The resulting framework provides health systems an outline of the resources, structures, criteria, and template documents to enable pre-implementation evaluation and post-implementation monitoring of AI solutions. Health systems will need to treat this or any alternative framework as a living document to maintain relevance and effectiveness as the AI landscape and regulations continue to evolve.

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Publication Details

Journal
npj Digital Medicine
Published
2025-08-11
DOI
https://doi.org/10.1038/s41746-025-01900-y
Citations
17
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
11.12

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article

A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare

George Wright, Mary Gagen, Justin Kramer, Timothy Hetherington et al.
17 citations
npj Digital Medicine
Artificial Intelligence in Healthcare and Education
11.12
article

A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare

George Wright, Mary Gagen, Justin Kramer, Timothy Hetherington, Brian J. Wells, Rikki Gaber Caffrey, Michael S. Carroll, Eva‐Carin Lindgren, E.Graham McKinley, Amanda B Kuzma, Katie Barr, Oğuz Akbilgiç, Lindsey Lonergan, Shih‐Hsiung Chou, Yhenneko J. Taylor, Antony F. Bowers, Eric S. Kirkendall, N. Chantelle Hardy, Hieu Nguyen, McKenzie Isreal, Andrew McWilliams, Stephen M. Downs, Nicholas M. Pajewski, Patricia Corn, Audrey Cuison, Matt Pallini, Amy Bovi, Matthew CiRullo, Jason Heuay, Kristina Katzovitz, Laura Sak-Castellano, Erika Setliff
article en
17 citations

Abstract

Health systems face the challenge of balancing innovation and safety to responsibly implement artificial intelligence (AI) solutions. The rapid proliferation, growing complexity, ethical considerations, and rising demand for these tools require timely and efficient processes for rigorous evaluation and ongoing monitoring. Current AI evaluation frameworks often lack the practical guidance for health systems to address these challenges. To fill this gap, we developed a prescriptive evaluation framework informed by a literature review, in-depth interviews with key stakeholders, including patients, and a multidisciplinary design workshop. The resulting framework provides health systems an outline of the resources, structures, criteria, and template documents to enable pre-implementation evaluation and post-implementation monitoring of AI solutions. Health systems will need to treat this or any alternative framework as a living document to maintain relevance and effectiveness as the AI landscape and regulations continue to evolve.

npj Digital MedicineVol. 8(1)
HealthEast Care System (US), Advocate Health Care (US), Office of the General Counsel (US), Atrium Medical Cente (US), Patient Advocate Foundation (US), Wake Forest University (US)
Duke Endowment
Openalex Percentile: Top 2%
Artificial Intelligence in Healthcare and Education
11.12
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