Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

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

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Adverse Childhood Experiences and Growth Outcomes in Childhood: A Longitudinal EHR-Based Study.

medRxiv : the preprint server for health sciences·2026

Same author

Charting the future of ACMI: a report from the 2025 ACMI symposium.

Journal of the American Medical Informatics Association : JAMIA·2026

Same author

Training a Nonclinician to Become a Leader in Transdisciplinary Clinical Research: Clinical Observerships.

International journal of clinical biostatistics and biometrics·2026

Same author

Developing an expanded version of My Diabetes Care in English and Spanish: A design and formative usability study.

Digital health·2026

Same author

Explainability in context: calibrating appropriate trust and reliance in artificial intelligence.

Journal of the American Medical Informatics Association : JAMIA·2026

Same author

Integrating genetically predicted transcriptomic signatures with longitudinal real-world data enables scalable drug repurposing for Alzheimer's disease.

Research square·2026

Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026

Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026

Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026

Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026

Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026

Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026

See all related articles

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:

Algorithmovigilance Artificial intelligence Human-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.