定义临床人工智能系统的操作安全性
Young-Tak Kim1, Hyunji Kim1, Manisha Bahl1
1Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
NPJ digital medicine
|February 20, 2026
概括
新的人工智能安全框架 (SA-ROC) 确定何时信任医疗保健中的人工智能. 它确定了自动化安全区和需要人类审查的灰色区,改善了临床AI的采用.
科学领域:
- 医疗人工智能 医疗人工智能
- 临床决策支持系统 临床决策支持系统
- 医疗信息学 医疗信息学
背景情况:
- 目前在临床环境中采用人工智能 (AI) 主要强调自动化.
- 传统的准确度指标无法充分解决人工智能系统可靠性和运营安全的关键问题.
- 评估人工智能安全需要的指标不仅仅是统计准确度.
研究的目的:
- 引入安全意识接收器操作特征 (SA-ROC) 框架,以定义和评估人工智能系统的操作安全性.
- 建立明确的标准,确定何时在临床工作流程中信任AI是安全的.
- 开发一个量化衡量与AI无决心相关的工作量.
主要方法:
- 开发了安全意识接收器操作特征 (SA-ROC) 框架,根据预先规定的可靠性级别来定义操作安全.
- 将SA-ROC曲线划分为规则安全区,规则安全区 (自主人工智能行动) 和灰色区 (强制人体审查).
- 引入了灰色区域区域 (ΓArea) 度量来量化人工智能犹和非自动化工作负载的运营成本.
主要成果:
- 在FDA批准的两种癌症查算法的案例研究中,SA-ROC框架显示了关键的逆转.
- 人工智能模型具有统计上优越的曲线下面面积 (AUC),在高可靠性查任务中运行安全性较差.
- 灰色区域区域 (ΓArea) 指标量化了人工智能犹工作负载的影响.
结论:
- SA-ROC框架通过将临床政策转化为优化的运营工作流程,使得在医疗保健中AI的积极治理成为可能.
- 这种方法补充了现有的监管安全评估,重点关注运营安全和可信度.
- SA-ROC为确保人工智能的安全和有效临床采用提供了一个至关重要的工具.
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