关于数字病理学人工智能产品的公开证据

Gillian A Matthews, Clare McGenity, Daljeet Bansal, Darren Treanor
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摘要

背景:商业市场上不断出现将人工智能(AI)方法应用于数字病理图像的新产品,这些产品标榜能提高诊断准确性、工作流程效率和治疗选择。然而,关于这些产品的公开信息可能是多变的,很少有来源可以获得独立的证据:我们的目标是识别和评估基于人工智能的数字病理学产品的公开证据。我们比较了欧洲经济区/大不列颠(EEA/GB)市场上产品的主要特征,包括其监管批准、预期用途和已发表的验证研究。我们将使用血红素和伊红(H&E)染色的组织图像作为输入、应用基于人工智能的方法来支持图像解读,并在 2023 年 9 月前获得监管部门批准的产品纳入了研究范围:结果:我们发现有 26 种基于人工智能的产品符合纳入标准。大多数产品(73%)专注于乳腺病理学或泌尿病理学,其主要功能是肿瘤或特征检测。在这 26 种产品中,有 24 种已通过自我认证途径获得了监管部门的批准,成为一般体外诊断 (IVD) 医疗器械,而这并不需要合格评定机构的独立审查。此外,只有 10 种产品(占 38%)与同行评审的科学出版物有关,这些出版物介绍了这些产品的开发和内部验证情况,而 11 种产品(占 42%)与同行评审的出版物有关,这些出版物介绍了外部验证情况(即对开发过程中使用的不同来源的数据进行测试):结论:数字病理学新产品的公开信息很难跟上快速发展的步伐。为了提高透明度,我们将监管部门批准的人工智能产品的现有公开证据收集到一个在线登记册中:https://resources.npic.uk/AI/ProductRegister。我们预计这将为新型设备提供可访问的资源,并为决定哪些产品可为患者带来益处提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Public evidence on AI products for digital pathology
Background: Novel products applying artificial intelligence (AI)-based approaches to digital pathology images have consistently emerged onto the commercial market, touting improvements in diagnostic accuracy, workflow efficiency, and treatment selection. However, publicly available information on these products can be variable, with few sources to obtain independent evidence. Methods: Our objective was to identify and assess the public evidence on AI-based products for digital pathology. We compared key features of products on the European Economic Area/Great Britain (EEA/GB) markets, including their regulatory approval, intended use, and published validation studies. We included products that used haematoxylin and eosin (H&E)-stained tissue images as input, applied an AI-based method to support image interpretation, and received regulatory approval by September 2023. Results: We identified 26 AI-based products that met our inclusion criteria. The majority (73%) were focused on breast pathology or uropathology, and their primary function was tumour or feature detection. Of the 26 products, 24 had received regulatory approval via the self-certification route as General in vitro diagnostic (IVD) medical devices, which does not require independent review by a conformity assessment body. Furthermore, only 10 of the products (38%) were associated with peer-reviewed scientific publications describing their development and internal validation, while 11 products (42%) had peer-reviewed publications describing external validation (i.e., testing on data from a source distinct to that used in development). Conclusions: The availability of public information on new products for digital pathology is struggling to keep up with the rapid pace of development. To support transparency, we gathered available public evidence on regulatory-approved AI products into an online register: https://resources.npic.uk/AI/ProductRegister. We anticipate this will provide an accessible resource on novel devices and support decisions on which products could bring benefit to patients.
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