Quality Assessment of Large Language Model–Generated Medical Dialogue for Clinical Vignettes: Evaluation Study
While physician oversight remains essential, it is feasible to efficiently create AI-generated educational materials for medical education that overcome the limitations of traditional clinical vignettes. This approach may reduce time and financial burdens, enhancing opportunities to practice clinical interviewing in settings that closely mirror real-world encounters.
Authors
- Daiki Yokokawa (ORCID: https://orcid.org/0000-0003-0944-8664)
- Yasuhisa Okano (ORCID: https://orcid.org/0009-0007-0582-776X)
- Yasutaka Yanagita (ORCID: https://orcid.org/0000-0002-9213-8247)
- Takanori Uehara (ORCID: https://orcid.org/0000-0001-5086-5799)
- R. Yoshida (ORCID: https://orcid.org/0000-0003-3657-683X)
- Shiichi Ihara (ORCID: https://orcid.org/0000-0002-5081-9327)
Institutions
- Chiba University Hospital (JP)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2025-10-15
- DOI
- https://doi.org/10.2196/80752
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00