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Detecting Algorithmic Errors and Patient Harms for AI-Enabled Medical Devices in Randomized Controlled Trials:

Aditya U Kale1,2,3,4, Henry David Jeffry Hogg5, Russell Pearson6

  • 1Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom.

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Summary
This summary is machine-generated.

This systematic review assesses artificial intelligence (AI) medical device errors and adverse events in clinical trials. It highlights the need for rigorous analysis of AI performance errors and patient harms to ensure safety across all populations.

Keywords:
AIAI health technologyadverse eventsalgorithm erroralgorithmicartificial intelligencemedical devicepatient safetyrandomized controlled trialssafetysystematic review

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Area of Science:

  • Medical Technology
  • Clinical Research
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) medical devices offer transformative potential in clinical workflows, diagnostics, prognostics, and treatment decisions.
  • Ensuring the safety of AI medical devices across diverse populations is critical.
  • Existing literature highlights the need for rigorous performance error analysis to prevent patient harm from issues like algorithmic bias or specific failure modes.

Purpose of the Study:

  • To systematically review the frequency and severity of AI errors and adverse events (AEs) in randomized controlled trials (RCTs) of AI medical devices.
  • To explore the methodologies used for analyzing AI performance errors, including subgroup outcome investigations.
  • To identify gaps in reporting and analysis of AI-related harms in clinical studies.

Main Methods:

  • Systematic review of RCTs evaluating AI medical devices, with searches in MEDLINE, Embase, Cochrane CENTRAL, and clinical trial registries.
  • Primary outcomes include the frequency and severity of AI errors, patient harms, and reported AEs.
  • Quality assessment using the Cochrane risk-of-bias tool (RoB2), with data analysis comparing error rates and patient harms between arms, and potential meta-analysis.

Main Results:

  • Preliminary searches and strategy design are complete; title/abstract screening began in September 2023, with full-text screening and data collection ongoing.
  • The review is registered on PROSPERO (CRD42023387747).
  • Data collection and analysis commenced in April 2024.

Conclusions:

  • Variable reporting of AI medical device studies necessitates standardized methods for evaluating safety.
  • Rigorous detection, analysis, and reporting of performance errors and patient harms are vital for robust AI safety assessment.
  • This review will provide insights into the frequency and severity of AI errors and harms, informing better analytical approaches for overall and subgroup performance.