Evaluating acute image ordering for real-world patient cases via language model alignment with radiological guidelines
Our work demonstrates and validates a strategy to leverage AI-based software to improve trustworthy clinical decision-making in alignment with expert evidence-based guidelines.
Authors
- James C. Gee (ORCID: https://orcid.org/0000-0002-2258-0187)
- Hersh Sagreiya (ORCID: https://orcid.org/0000-0002-2909-6793)
- Osbert Bastani (ORCID: https://orcid.org/0000-0001-9990-7566)
- Allison Chae (ORCID: https://orcid.org/0000-0001-7029-556X)
- Walter R T Witschey (ORCID: https://orcid.org/0000-0003-1669-2120)
- Piya V. Saraiya
- Michael S. Yao (ORCID: https://orcid.org/0000-0002-7008-6028)
- Charles E. Kahn (ORCID: https://orcid.org/0000-0002-6654-7434)
Institutions
- University of Pennsylvania (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-08-04
- DOI
- https://doi.org/10.1038/s43856-025-01061-9
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Alpha Omega Alpha Honor Medical Society
- University of Pennsylvania
- Institute for Translational Medicine and Therapeutics
- National Institutes of Health
- National Institute of Biomedical Imaging and Bioengineering
- Division of Computing and Communication Foundations
- National Center for Advancing Translational Sciences
- National Institute on Minority Health and Health Disparities