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

Authors

Brian J. Wells, Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Hieu M. Nguyen, Center for Health System Sciences, Atrium Health, Charlotte, NCFollow
Andrew McWilliams, Department of Internal Medicine, Division of Hospital Medicine, Atrium Health, Charlotte, NC, USA.
Matt Pallini, Advanced Analytics, Enterprise Data Science, Advocate Health, Charlotte, NC, USA.Follow
Amy Bovi, Advocate Health - MidwestFollow
Andrew Kuzma, Advocate Health - MidwestFollow
Justin Kramer, Department of Family and Community Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Shih-Hsiung Chou, Advanced Analytics, Enterprise Data Science, Advocate Health, Charlotte, NC, USA.Follow
Timothy Hetherington, Advanced Analytics, Enterprise Data Science, Advocate Health, Charlotte, NC, USA.Follow
Patricia Corn, Compliance and Integrity, Advocate Health, Winston-Salem, NC, USA.Follow
Yhenneko J. Taylor, Center for Health System Sciences, Atrium Health, Charlotte, NC, USA.
Audrey Cuison, Advanced Analytics, Advocate Health, Charlotte, NC, USA.Follow
Mary Gagen, Advanced Analytics, Advocate Health, Charlotte, NC, USA.Follow
McKenzie Isreal, Center for Health System Sciences, Atrium Health, Charlotte, NC, USA.
FAIR-AI Consortium

Recommended Citation

Wells BJ, Nguyen HM, McWilliams A, et al. A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare. NPJ Digit Med. 2025;8(1):514. Published 2025 Aug 11. doi:10.1038/s41746-025-01900-y

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.

Document Type

Article

PubMed ID

40790350