学术医疗中心联合学习风险和治理的务实框架
Daniel Bottomly1, Bridget Barnes2, Kuli Mavuwa2
1OHSU Knight Cancer Institute, 3485 S Bond Ave, Portland, OR, 97239, United States, 1 503-494-8311.
JMIR AI
|March 11, 2026
概括
联合学习 (FL) 使学术医疗中心 (AMC) 能够在不共享敏感患者数据的情况下进行协作AI模型开发. 这种方法解决了数据隐私问题,但引入了新的治理和安全风险,需要专门的管理框架.
科学领域:
- 人工智能在医学中的应用
- 联邦学习学习 (Federated Learning) 是一种学习方式.
- 健康数据治理 治理健康数据
背景情况:
- 由于数据量和隐私问题,学术医学中心 (AMC) 在开发高性能AI模型方面面临着挑战.
- 生物医学数据库的分散可能会阻碍有效的数据共享和AI模型开发.
- 联合学习 (FL) 成为协作人工智能培训的解决方案,同时保持数据隐私.
研究的目的:
- 解决与AMC中的FL实施相关的新型治理,安全和运营风险.
- 为生物医学研究和临床环境中管理FL风险提供基于标准的框架.
- 支持AMC领导人在跨机构人工智能开发的复杂性中进行导航.
主要方法:
- 开发了一个风险差异化框架和一个FL风险矩阵.
- 创建了关键的治理文物,将其映射到机构挑战中.
- 拟议的工具与国际标准 (NIST AI RMF,ISO/IEC 42001) 和AMC领导经验保持一致.
主要成果:
- 提出了FL风险管理的务实,说明性指南,而不是规范性检查清单.
- 提供基于领先的国际标准和现实世界的治理经验的工具.
- 促进了FL治理与关键机构挑战的协调.
结论:
- FL是医疗保健领域多机构AI发展的关键途径,平衡创新与数据隐私.
- 现有的程序不足以管理AMC中的FL特异性风险.
- 拟议的框架和文物为AMC领导人提供了一个新的资源,以有效地管理不断变化的FL风险.
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