医疗保健中的联合学习:从研究到现实世界的部署
Spyridon Bakas1,2,3,4,5, Xiaoxiao Li6,7, Prashant Shah5
11Center for Federated Learning in Medicine, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA;
Annual review of biomedical engineering
|January 21, 2026
概括
联合学习 (FL) 能够实现跨机构的协作AI模型培训,而无需共享敏感患者数据,克服临床AI实施的障碍. 这种方法在医疗保健中推进了公平和有影响力的AI.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能 (AI) 对医疗保健有希望,但由于数据访问限制,在临床翻译方面面临挑战.
- 针对多机构合作的集中式学习模式受到隐私,法律和后勤问题的阻碍.
- 联合学习 (FL) 提供了一个去中心化的解决方案,用于在不共享原始数据的情况下进行协作AI开发.
研究的目的:
- 审查生物医学应用联合学习的算法,隐私和实际进展.
- 讨论处理数据异质性的策略,并确保FL的隐私.
- 确定在医疗保健中可扩展FL部署的局限性和基础设施需求.
主要方法:
- 审查生物医学工程联合学习算法的关键发展.
- 分析隐私保护技术,包括差异隐私,安全聚合和保密计算.
- 讨论基础设施和互操作性的实际考虑.
主要成果:
- 联合学习促进了协作模式培训,同时保护了患者数据的隐私.
- 已有策略解决跨机构的非相同数据分布问题.
- 可扩展和可互操作的基础设施对于FL的广泛采用至关重要.
结论:
- 联合学习代表了发展可泛化,公平和临床影响力的人工智能模型的范式转变.
- 需要在FL-as-a-service平台和符合监管的工作流程方面继续取得进展.
- 对FL模型的可靠和持续部署是实现AI在患者护理中的潜力的关键.
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