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Related Experiment Video

Updated: Jun 21, 2025

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AI as a Medical Device Adverse Event Reporting in Regulatory Databases: Protocol for a Systematic Review.

Aditya U Kale1,2,3,4, Riya Dattani5, Ashley Tabansi6

  • 1Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom.

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Summary
This summary is machine-generated.

This study reviews adverse events (AEs) reported for artificial intelligence as a medical device (AIaMD) across three regulatory databases. It aims to improve AIaMD safety monitoring by identifying reporting gaps and characterizing event types.

Keywords:
adverse eventartificial intelligenceartificial intelligence health technologydescriptive analysisfeedbackhealth care productmedical devicesregulatory databaseregulatory sciencereporting systemriskssafetysafety issuesafety monitoring

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Area of Science:

  • Medical device safety
  • Artificial intelligence in healthcare
  • Regulatory science

Background:

  • Adverse event (AE) reporting for medical devices is suboptimal due to recognition, knowledge, and cultural barriers.
  • Artificial intelligence as a medical device (AIaMD) introduces unique risks like algorithmic bias, necessitating robust safety monitoring.
  • Current AE reporting systems have limitations in detecting, attributing, and reporting safety signals for AIaMD.

Purpose of the Study:

  • To systematically review and characterize adverse events (AEs) reported for AIaMD across major regulatory databases.
  • To identify the frequency and types of AEs associated with AIaMD.
  • To understand limitations in current AE reporting for AIaMD and inform future safety monitoring strategies.

Main Methods:

  • Systematic review of publicly accessible AE databases from the United States, United Kingdom, and Australia.
  • Inclusion criteria: AE reports involving AI medical devices; exclusion: software as a medical device without AI.
  • Data extraction and descriptive analysis to characterize AE types, frequency, and severity according to regulatory guidance.

Main Results:

  • Scoping searches are complete, with screening beginning April 2024 and data extraction/synthesis from May to August 2024.
  • The review will highlight AE types reported for AIaMD and identify reporting gaps.
  • Anticipated finding: particularly low reporting rates for indirect harms associated with AIaMD.

Conclusions:

  • This systematic review is the first to analyze AE reports for AIaMD from three regulatory sources.
  • The study will provide real-world evidence on AIaMD safety but acknowledges limitations due to database opacity.
  • Findings will aid regulators and policymakers in enhancing AIaMD safety monitoring processes.