评估FDA审查的医疗器械AI/ML模型特征的透明度
Viraj Mehta1, Abhinav Komanduri2, Rishabh Singh Bhadouriya3
1Stanford University, Department of Computer Science, Stanford, USA, CA.
NPJ digital medicine
|November 17, 2025
概括
人工智能/ML医疗器械的透明度很低,对开发和性能的报告很差. 最近的FDA指导方针显示只有适度的改进,强调需要强制执行的标准来建立信任.
科学领域:
- 医疗技术 医疗技术 医学技术
- 监管科学 监管科学
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在医疗设备中的集成需要透明的监管报告.
- 美国食品和药物管理局 (FDA) 在2021年引入了良好的机器学习实践 (GMLP) 指南,以解决这一需求.
- 在FDA审查的AI/ML医疗器械中,遵守GMLP原则仍然存在不确定性.
研究的目的:
- 评估FDA批准的支持AI/ML的医疗器械监管报告的透明度.
- 评估2021年GMLP指南对报告透明度的影响.
- 识别与模型开发和绩效指标有关的透明度缺口.
主要方法:
- 对1970年至2024年12月期间批准的AI/ML设备的1,012份FDA安全性和有效性摘要 (SSED) 的审查.
- 在17个类别中使用了一种新的AI特征透明度报告 (ACTR) 评分来量化透明度.
- 分析了2021年GMLP指南发布前后ACTR分数的变化.
主要成果:
- 平均ACTR得分低至17分之3.3,表明存在重大透明度缺陷.
- 在ACTR分数中观察到0.88点的适度改善,这是2021年后的指导方针.
- 几乎一半的设备缺乏临床研究报告,超过一半的设备遗漏了性能指标.
结论:
- 在支持AI/ML的医疗器械的监管报告中存在显著的透明度差距.
- 目前对GMLP原则的遵守不足以确保足够的透明度.
- 强制执行的标准对于增强信任和确保AI/ML医疗技术的安全至关重要.
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