Simulated evaluation of large language model stepwise diagnostic reasoning with real-world chest pain encounters and Bayesian networks
In this simulated assessment, GPT-4o demonstrated diagnostic biases toward rare conditions and differed substantially from normative probabilistic models and physician practice patterns. These discrepancies could lead to unnecessary over-triage and resource utilization. Integrating LLMs within more rigorous probabilistic frameworks and calibrating them to realistic disease prevalences may be essential for effectively harnessing their potential as clinical decision-support tools.
Authors
- David Chartash (ORCID: https://orcid.org/0000-0002-0265-330X)
- D. Wright (ORCID: https://orcid.org/0000-0002-2564-7754)
- Alaa Alashi
- Vimig Socrates (ORCID: https://orcid.org/0000-0001-7955-9875)
- Kent McCann
- Thomas Huang
- R. Andrew Taylor
- Conrad W. Safranek
Institutions
- Yale University (US)
- Yale New Haven Health System (US)
- University of Virginia (US)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2026-02-24
- DOI
- https://doi.org/10.1186/s12911-026-03381-9
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00