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©Aditya U Kale, Riya Dattani, Ashley Tabansi, Henry David Jeffry Hogg, Russell Pearson, Ben Glocker, Su Golder, Justin Waring, Xiaoxuan Liu, David J Moore, Alastair K Denniston. Originally published in JMIR Research Protocols.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.
Author(s): Kale AU, Dattani R, Tabansi A, Hogg HDJ, Pearson R, Glocker B, Golder S, Waring J, Liu X, Moore DJ, Denniston AK
Publication type: Article
Publication status: Published
Journal: JMIR Research Protocols
Year: 2024
Volume: 13
Online publication date: 11/07/2024
Acceptance date: 01/05/2024
Date deposited: 27/08/2024
ISSN (electronic): 1929-0748
Publisher: JMIR Publications Inc.
URL: https://doi.org/10.2196/48156
DOI: 10.2196/48156
PubMed id: 38990628
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