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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月11日

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  • Brian J. Wells,
  • Hieu M. Nguyen,
  • Andrew McWilliams,
  • Matt Pallini,
  • Amy Bovi,
  • Andrew Kuzma,
  • Justin Kramer,
  • Shih-Hsiung Chou,
  • Timothy Hetherington,
  • Patricia Corn,
  • Yhenneko J. Taylor,
  • Audrey Cuison,
  • Mary Gagen,
  • McKenzie Isreal,
  • Oguz Akbilgic,
  • Katie Barr,
  • Alicia Bowers,
  • Rikki Caffrey,
  • Michael S. Carroll,
  • Matthew CiRullo,
  • Stephen M. Downs,
  • Natalie Hardy,
  • Jason Heuay,
  • Kristina Katzovitz,
  • Eric Kirkendall,
  • Elsie Lindgren,
  • Lindsey Lonergan,
  • Elissa McKinley,
  • Nicholas M. Pajewski,
  • Laura Sak-Castellano,
  • Erika Setliff,
  • Gabe Wright

摘要

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.