Artificial intelligence in hepatology: a comparative analysis of ChatGPT-4, Bing, and Bard at answering clinical questions
LLMs demonstrate variable accuracy when answering clinical questions related to hepatology, though show comparable efficacy when presented with questions in an open-ended versus multiple choice (MCQ) format. Further research is required to investigate the optimal use of LLMs in clinical and educational contexts.
Authors
- Sama Anvari (ORCID: https://orcid.org/0000-0002-7284-8735)
- Sarah Malone (ORCID: https://orcid.org/0009-0005-3469-7680)
- Yung Lee (ORCID: https://orcid.org/0000-0002-6257-4400)
- David Jin (ORCID: https://orcid.org/0009-0005-5257-3577)
- Matthew Collins
Institutions
- Queens University (BD)
- St. Joseph’s Healthcare Hamilton (CA)
- McMaster University (CA)
Publication Details
- Journal
- Journal of the Canadian Association of Gastroenterology
- Published
- 2025-02-22
- DOI
- https://doi.org/10.1093/jcag/gwae055
- Citations
- 3
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.96