ArticleJournal of general internal medicine2024
Can AI-Generated Clinical Vignettes in Japanese Be Used Medically and Linguistically?
Article in Journal of general internal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial.BMC medical education · 2024Trial
- A scoping review of the use of generative artificial intelligence tools in health profession education.BMC medical education · 2026Article
- AI-Driven Objective Structured Clinical Examination Generation in Digital Health Education: Comparative Analysis of Three GPT-4o Configurations.JMIR medical education · 2026Article
- Mapping applications and evaluations of LLM-enabled AI chatbots for health purposes: a scoping review.Frontiers in public health · 2026Article
- Large Language Model-Based Patient Simulation to Foster Communication Skills in Health Care Professionals: User-Centered Development and Usability Study.JMIR medical education · 2025Article
- Quality Assessment of Large Language Model-Generated Medical Dialogue for Clinical Vignettes: Evaluation Study.JMIR formative research · 2025Article
- Educational Utility of Clinical Vignettes Generated in Japanese by ChatGPT-4: Mixed Methods Study.JMIR medical education · 2024Article
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6 authors.
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Abstract
backgroundCreating clinical vignettes requires considerable effort. Recent developments in generative artificial intelligence (AI) for natural language processing have been remarkable and may allow for the easy and immediate creation of diverse clinical vignettes.
objectiveIn this study, we evaluated the medical accuracy and grammatical correctness of AI-generated clinical vignettes in Japanese and verified their usefulness.
methodsClinical vignettes were created using the generative AI model GPT-4-0613. The input prompts for the clinical vignettes specified the following seven elements: (1) age, (2) sex, (3) chief complaint and time course since onset, (4) physical findings, (5) examination results, (6) diagnosis, and (7) treatment course. The list of diseases integrated into the vignettes was based on 202 cases considered in the management of diseases and symptoms in Japan's Primary Care Physicians Training Program. The clinical vignettes were evaluated for medical and Japanese-language accuracy by three physicians using a five-point scale. A total score of 13 points or above was defined as "sufficiently beneficial and immediately usable with minor revisions," a score between 10 and 12 points was defined as "partly insufficient and in need of modifications," and a score of 9 points or below was defined as "insufficient."
resultsRegarding medical accuracy, of the 202 clinical vignettes, 118 scored 13 points or above, 78 scored between 10 and 12 points, and 6 scored 9 points or below. Regarding Japanese-language accuracy, 142 vignettes scored 13 points or above, 56 scored between 10 and 12 points, and 4 scored 9 points or below. Overall, 97% (196/202) of vignettes were available with some modifications.
conclusionOverall, 97% of the clinical vignettes proved practically useful, based on confirmation and revision by Japanese medical physicians. Given the significant effort required by physicians to create vignettes without AI, using GPT is expected to greatly optimize this process.
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