PDF(Pubmed)
Abstract：
BACKGROUND: The reporting of adverse events (AEs) relating to medical devices is a long-standing area of concern, with suboptimal reporting due to a range of factors including a failure to recognize the association of AEs with medical devices, lack of knowledge of how to report AEs, and a general culture of nonreporting. The introduction of artificial intelligence as a medical device (AIaMD) requires a robust safety monitoring environment that recognizes both generic risks of a medical device and some of the increasingly recognized risks of AIaMD (such as algorithmic bias). There is an urgent need to understand the limitations of current AE reporting systems and explore potential mechanisms for how AEs could be detected, attributed, and reported with a view to improving the early detection of safety signals.
OBJECTIVE: The systematic review outlined in this protocol aims to yield insights into the frequency and severity of AEs while characterizing the events using existing regulatory guidance.
METHODS: Publicly accessible AE databases will be searched to identify AE reports for AIaMD. Scoping searches have identified 3 regulatory territories for which public access to AE reports is provided: the United States, the United Kingdom, and Australia. AEs will be included for analysis if an artificial intelligence (AI) medical device is involved. Software as a medical device without AI is not within the scope of this review. Data extraction will be conducted using a data extraction tool designed for this review and will be done independently by AUK and a second reviewer. Descriptive analysis will be conducted to identify the types of AEs being reported, and their frequency, for different types of AIaMD. AEs will be analyzed and characterized according to existing regulatory guidance.
RESULTS: Scoping searches are being conducted with screening to begin in April 2024. Data extraction and synthesis will commence in May 2024, with planned completion by August 2024. The review will highlight the types of AEs being reported for different types of AI medical devices and where the gaps are. It is anticipated that there will be particularly low rates of reporting for indirect harms associated with AIaMD.
CONCLUSIONS: To our knowledge, this will be the first systematic review of 3 different regulatory sources reporting AEs associated with AIaMD. The review will focus on real-world evidence, which brings certain limitations, compounded by the opacity of regulatory databases generally. The review will outline the characteristics and frequency of AEs reported for AIaMD and help regulators and policy makers to continue developing robust safety monitoring processes.
UNASSIGNED: PRR1-10.2196/48156.
OBJECTIVE: The systematic review outlined in this protocol aims to yield insights into the frequency and severity of AEs while characterizing the events using existing regulatory guidance.
METHODS: Publicly accessible AE databases will be searched to identify AE reports for AIaMD. Scoping searches have identified 3 regulatory territories for which public access to AE reports is provided: the United States, the United Kingdom, and Australia. AEs will be included for analysis if an artificial intelligence (AI) medical device is involved. Software as a medical device without AI is not within the scope of this review. Data extraction will be conducted using a data extraction tool designed for this review and will be done independently by AUK and a second reviewer. Descriptive analysis will be conducted to identify the types of AEs being reported, and their frequency, for different types of AIaMD. AEs will be analyzed and characterized according to existing regulatory guidance.
RESULTS: Scoping searches are being conducted with screening to begin in April 2024. Data extraction and synthesis will commence in May 2024, with planned completion by August 2024. The review will highlight the types of AEs being reported for different types of AI medical devices and where the gaps are. It is anticipated that there will be particularly low rates of reporting for indirect harms associated with AIaMD.
CONCLUSIONS: To our knowledge, this will be the first systematic review of 3 different regulatory sources reporting AEs associated with AIaMD. The review will focus on real-world evidence, which brings certain limitations, compounded by the opacity of regulatory databases generally. The review will outline the characteristics and frequency of AEs reported for AIaMD and help regulators and policy makers to continue developing robust safety monitoring processes.
UNASSIGNED: PRR1-10.2196/48156.
摘要：
背景：与医疗器械相关的不良事件（AE）的报告是一个长期关注的领域，由于一系列因素，包括未能认识到不良事件与医疗设备的关联，报告效果欠佳，缺乏如何报告AE的知识，和一般的不报告文化。人工智能作为医疗设备（AIaMD）的引入需要一个强大的安全监控环境，该环境既可以识别医疗设备的一般风险，也可以识别AIaMD的一些日益被认可的风险（例如算法偏差）。迫切需要了解当前AE报告系统的局限性，并探索如何检测AE的潜在机制。归因,并报告以改善安全信号的早期检测。
目的：本方案中概述的系统评价旨在利用现有的监管指导来描述事件的发生频率和严重程度。
方法：将检索可公开访问的AE数据库，以确定AIaMD的AE报告。范围搜索已经确定了3个监管区域，这些区域提供了公众对AE报告的访问：美国，联合王国,和澳大利亚。如果涉及人工智能(AI)医疗设备，将包括AE进行分析。作为没有人工智能的医疗设备的软件不在本审查的范围内。数据提取将使用为此审查设计的数据提取工具进行，并将由AUK和第二位审查者独立完成。将进行描述性分析，以确定报告的不良事件类型，和他们的频率，对于不同类型的AIaMD。将根据现有的监管指导对AE进行分析和表征。
结果：范围搜索正在进行，筛查将于2024年4月开始。数据提取和合成将于2024年5月开始，计划于2024年8月完成。该审查将重点介绍针对不同类型的AI医疗设备报告的AE类型以及差距所在。预计与AIaMD相关的间接损害的报告率将特别低。
结论：据我们所知，这将是对3个不同监管来源报告的与AIaMD相关的AE的首次系统评价.审查将集中在现实世界的证据，这带来了某些限制，再加上监管数据库的不透明度。该审查将概述AIaMD报告的AE的特征和频率，并帮助监管机构和政策制定者继续开发强大的安全监控流程。
■PRR1-10.2196/48156。
目的：本方案中概述的系统评价旨在利用现有的监管指导来描述事件的发生频率和严重程度。
方法：将检索可公开访问的AE数据库，以确定AIaMD的AE报告。范围搜索已经确定了3个监管区域，这些区域提供了公众对AE报告的访问：美国，联合王国,和澳大利亚。如果涉及人工智能(AI)医疗设备，将包括AE进行分析。作为没有人工智能的医疗设备的软件不在本审查的范围内。数据提取将使用为此审查设计的数据提取工具进行，并将由AUK和第二位审查者独立完成。将进行描述性分析，以确定报告的不良事件类型，和他们的频率，对于不同类型的AIaMD。将根据现有的监管指导对AE进行分析和表征。
结果：范围搜索正在进行，筛查将于2024年4月开始。数据提取和合成将于2024年5月开始，计划于2024年8月完成。该审查将重点介绍针对不同类型的AI医疗设备报告的AE类型以及差距所在。预计与AIaMD相关的间接损害的报告率将特别低。
结论：据我们所知，这将是对3个不同监管来源报告的与AIaMD相关的AE的首次系统评价.审查将集中在现实世界的证据，这带来了某些限制，再加上监管数据库的不透明度。该审查将概述AIaMD报告的AE的特征和频率，并帮助监管机构和政策制定者继续开发强大的安全监控流程。
■PRR1-10.2196/48156。
