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Related Experiment Videos

Beyond Model Performance: Information Needs for an Algorithmovigilance Sociotechnical System.

Megan E Salwei1, Sharon Davis1, Laurie L Novak1

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.

Studies in Health Technology and Informatics
|May 17, 2025
PubMed
Summary

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Ongoing monitoring of artificial intelligence (AI) systems, known as algorithmovigilance, is crucial for patient safety in healthcare. This study outlines the necessary information for AI monitoring systems and offers design guidance for effective algorithmovigilance.

Area of Science:

  • Healthcare Informatics
  • Artificial Intelligence Safety
  • Clinical Decision Support

Background:

  • Artificial intelligence (AI) integration in healthcare presents novel patient safety challenges.
  • Proactive monitoring of AI systems is necessary to manage these risks effectively.

Purpose of the Study:

  • To define the information requirements for an AI monitoring and operations system.
  • To provide design guidelines for developing robust algorithmovigilance systems.

Main Methods:

  • Literature review on AI monitoring needs.
  • Analysis of information requirements for operational AI systems.
  • Development of design principles for algorithmovigilance.

Main Results:

Keywords:
AlgorithmovigilanceArtificial intelligenceHuman-Centered Design

Related Experiment Videos

  • Identified key data points and operational parameters for AI system monitoring.
  • Outlined essential functionalities for an AI monitoring platform.
  • Proposed a framework for algorithmovigilance system design.

Conclusions:

  • Algorithmovigilance is critical for ensuring the safe deployment of AI in clinical settings.
  • Well-designed monitoring systems are essential for mitigating AI-related patient safety risks.