通过基于系统审查的综合成熟度模型推进医疗保健人工智能治理
Rowan Hussein1, Anna Zink2,3, Bashar Ramadan2
1Center for Computational Medicine and Clinical Artificial Intelligence, Department of Medicine, University of Chicago, Chicago, IL, USA. rowan.hussein@uchicagomedicine.org.
NPJ digital medicine
|February 11, 2026
概括
医疗机构需要对人工智能的适应性治理. 一个新的准备性评估 (HAIRA) 为人工智能实施提供了以资源为基础的途径,确保了所有组织规模的利益.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 医疗保健管理的管理
背景情况:
- 加快人工智能 (AI) 在医疗保健中的部署受到分散的治理框架的挑战,通常需要大量的资源.
- 现有的人工智能治理模型对较小的医疗保健组织构成障碍,原因是对资源的需求很高.
研究的目的:
- 开发一个适应性治理框架,解决医疗保健人工智能实施中的资源差异.
- 引入医疗保健AI治理准备评估 (HAIRA) 成熟模型.
主要方法:
- 对35个医疗保健人工智能实施治理框架 (2019-2024) 的系统审查.
- 在医疗保健AI治理中识别了七个关键领域.
- 将发现组织成一个五级成熟度模型 (HAIRA).
主要成果:
- 在审查的框架中确定了医疗保健人工智能治理的七个关键领域.
- 哈伊拉模型提供了一种分层的方法 (一至五级),具有适应性治理的基准.
- 根据组织的资源水平,HAIRA提供了可操作的途径.
结论:
- 哈伊拉模型为医疗保健人工智能治理提供了一个可扩展的解决方案,可以容纳不同资源能力的组织.
- 该框架支持公平的人工智能实施,确保整个医疗保健系统获得实实在在的好处.
- 哈伊拉促进了战略评估和对人工智能治理能力的有针对性的推进.
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