Babic Boris, Glenn Cohen I, Stern Ariel Dora, Li Yiwen, Ouellet Melissa
University of Hong Kong, Hong Kong, SAR, China.
University of Toronto, Toronto, ON, Canada.
NPJ Digit Med. 2025 May 31;8(1):328. doi: 10.1038/s41746-025-01717-9.
This project represents the first systematic assessment of the US Food and Drug Administration's postmarket surveillance of legally marketed artificial intelligence and machine learning based medical devices. We focus on the Manufacturer and User Facility Device Experience database-the FDA's central tool for tracking the safety of marketed AI/ML devices. In particular, we evaluate the data pertaining to adverse events associated with approximately 950 medical devices incorporating AI/ML functions for devices approved between 2010 through 2023, and we find that the existing system is insufficient for properly assessing the safety and effectiveness of AI/ML devices. In particular, we make three contributions: (1) characterize the adverse event reports for such devices, (2) examine the ways in which the existing FDA adverse reporting system for medical devices falls short, and (3) suggest changes FDA might consider in its approach to adverse event reporting for devices incorporating AI/ML functions.
该项目是对美国食品药品监督管理局（FDA）对合法上市的基于人工智能和机器学习的医疗设备进行上市后监管的首次系统性评估。我们重点关注制造商和用户设施设备经验数据库——FDA追踪上市人工智能/机器学习设备安全性的核心工具。具体而言，我们评估了与2010年至2023年期间批准的约950种包含人工智能/机器学习功能的医疗设备相关的不良事件数据，发现现有系统不足以正确评估人工智能/机器学习设备的安全性和有效性。特别是，我们做出了三项贡献：（1）描述此类设备的不良事件报告特征，（2）研究FDA现有的医疗器械不良报告系统存在不足的方面，（3）建议FDA在处理包含人工智能/机器学习功能的设备的不良事件报告时可能考虑的变化。
