Beyond Model Performance: Information Needs for an Algorithmovigilance Sociotechnical System
Proactive and ongoing monitoring of AI systems, or algorithmovigilance, is essential for mitigating patient safety risks from AI in healthcare. In this study, we describe the information needs for an AI monitoring and operations system and provide design guidelines for algorithmovigilance systems.
Authors
- Peter J. Embí (ORCID: https://orcid.org/0000-0002-7733-0847)
- Carrie Reale (ORCID: https://orcid.org/0000-0002-9011-2692)
- Russ Beebe (ORCID: https://orcid.org/0000-0001-5488-8234)
- Megan Elizabeth Salwei (ORCID: https://orcid.org/0000-0002-5457-2501)
- Susannah Leigh Rose (ORCID: https://orcid.org/0000-0001-5895-7460)
- Sharon E. Davis (ORCID: https://orcid.org/0000-0003-0792-8867)
- Colin G. Walsh (ORCID: https://orcid.org/0000-0002-9379-2056)
- Laurie Lovett Novak (ORCID: https://orcid.org/0000-0002-0415-4301)
- Scott D. Nelson (ORCID: https://orcid.org/0000-0002-1941-1817)
Institutions
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Studies in health technology and informatics
- Published
- 2025-05-15
- DOI
- https://doi.org/10.3233/shti250358
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00