Automated computation of the HEART score with the GPT-4 large language model
Automated risk score computation with language models has the potential to power interventions such as clinical decision support but has systematic differences from physician judgment. Prospective investigation is needed.
Authors
- David Chartash (ORCID: https://orcid.org/0000-0002-0265-330X)
- Rohit B. Sangal (ORCID: https://orcid.org/0000-0002-0435-7029)
- Monisha Dilip (ORCID: https://orcid.org/0000-0002-6946-1409)
- Catherine Xie Wright (ORCID: https://orcid.org/0000-0001-7638-4053)
- D. Wright (ORCID: https://orcid.org/0000-0002-2564-7754)
- Conrad Safranek (ORCID: https://orcid.org/0000-0003-1985-9432)
- Nickolas Srica
- Zachary Boivin (ORCID: https://orcid.org/0000-0003-4453-3282)
- Attila Feher
- Thomas Huang (ORCID: https://orcid.org/0000-0001-9056-7016)
- Vimig Socrates (ORCID: https://orcid.org/0000-0001-7955-9875)
- Edward Joseph Miller (ORCID: https://orcid.org/0000-0002-2156-5962)
- Richard A. Taylor (ORCID: https://orcid.org/0000-0002-9082-6644)
Institutions
- Yale University (US)
- University of Virginia (US)
Publication Details
- Journal
- The American Journal of Emergency Medicine
- Published
- 2025-04-03
- DOI
- https://doi.org/10.1016/j.ajem.2025.03.065
- Citations
- 5
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 12.40
Funders
- Blanche Moore Foundation
- Beckman Coulter Foundation
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke