Post-deployment Monitoring of AI Performance in Intracranial Hemorrhage Detection by ChatGPT
This study underscores the importance of continuous performance monitoring for AI systems in clinical practice. The integration of LLMs offers a scalable solution for evaluating AI performance, ensuring reliable deployment and enhancing diagnostic workflows.
Authors
- Nina Kottler
- Carlos A. Zuluaga (ORCID: https://orcid.org/0000-0002-1773-767X)
- Walter F. Wiggins (ORCID: https://orcid.org/0000-0002-0258-2708)
- Mohadese Ahmadzade (ORCID: https://orcid.org/0000-0001-7184-0275)
- Eric Rohren (ORCID: https://orcid.org/0000-0002-9788-6471)
- Sara Colella (ORCID: https://orcid.org/0000-0002-6731-5774)
- Neelesh Rastogi (ORCID: https://orcid.org/0000-0001-7092-9366)
- Jason Poff
- Phil Ramis
- Mohammad Ghasemi-rad
- John Yee
- Sriyesh Krishnan
Institutions
- Baylor College of Medicine (US)
- HealthPartners (US)
Publication Details
- Journal
- Academic Radiology
- Published
- 2025-08-12
- DOI
- https://doi.org/10.1016/j.acra.2025.07.055
- Citations
- 3
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.96