Institute for Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland; Department of Nuclear Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Institute for Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland; Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, CA, USA.
Lancet Digit Health. 2023 Sep;5(9):e618-e626. doi: 10.1016/S2589-7500(23)00126-7.
The US Food and Drug Administration is clearing an increasing number of artificial intelligence and machine learning (AI/ML)-based medical devices through the 510(k) pathway. This pathway allows clearance if the device is substantially equivalent to a former cleared device (ie, predicate). We analysed the predicate networks of cleared AI/ML-based medical devices (cleared between 2019 and 2021), their underlying tasks, and recalls. More than a third of cleared AI/ML-based medical devices originated from non-AI/ML-based medical devices in the first generation. Devices with the longest time since the last predicate device with an AI/ML component were haematology (2001), radiology (2001), and cardiovascular devices (2008). Especially for devices in radiology, the AI/ML tasks changed frequently along the device's predicate network, raising safety concerns. To date, only a few recalls might have affected the AI/ML components. To improve patient care, a stronger focus should be placed on the distinctive characteristics of AI/ML when defining substantial equivalence between a new AI/ML-based medical device and predicate devices.
美国食品和药物管理局正在通过 510(k)途径越来越多地批准人工智能和机器学习 (AI/ML) 为基础的医疗设备。如果该设备与以前已批准的设备（即前导设备）基本等效，则可以通过该途径获得批准。我们分析了已批准的基于 AI/ML 的医疗设备的前导网络（在 2019 年至 2021 年期间获得批准）、其基础任务和召回情况。超过三分之一的已批准的基于 AI/ML 的医疗设备来自第一代非 AI/ML 为基础的医疗设备。具有 AI/ML 组件的最后一个前导设备以来时间最长的设备是血液学（2001 年）、放射学（2001 年）和心血管设备（2008 年）。特别是对于放射学设备，AI/ML 任务在前导网络中经常发生变化，引起了安全问题。迄今为止，可能只有少数召回事件影响了 AI/ML 组件。为了改善患者护理，在定义新的基于 AI/ML 的医疗设备与前导设备之间的实质性等效性时，应更加关注 AI/ML 的独特特征。
