The Role of Large Language Models in Teaching Psychiatric Semiology: A Systematic Review

Teaching psychiatric semiology faces challenges such as the limited availability of real patients for educational purposes and a shortage of specialized instructors. This systematic review investigated the applications of Large Language Models (LLMs), exemplified by ChatGPT, to address these gaps.

Studies published between November 2022 and September 17, 2025, were identified through searches in PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, EBSCO, BVS, and EMBASE databases. Two independent reviewers conducted the systematic review following the PRISMA 2020 checklist, and the methodological quality of the included studies was individually assessed using the Mixed Methods Appraisal Tool (MMAT).

Of the 1,549 studies initially identified, two met the inclusion criteria, involving medical students and educators in Canada and Switzerland. LLMs demonstrated performance comparable to human experts in creating diagnostic tests and clinical vignettes but exhibited occasional simplifications and algorithmic biases. Participants reported positive perceptions of the tools' efficiency and practicality, but emphasized the need for specialized supervision and attention to potential privacy issues and superficiality in complex cases.

LLMs show potential as valuable supplementary resources for teaching psychiatric semiology, especially in settings with shortages of teachers and available patients for training. However, their limitations, including cultural and algorithmic biases, potential privacy risks, and superficial content generation, require close monitoring by experienced professionals. Although current evidence is preliminary, prospects are promising, highlighting the need for more robust and long-term studies to evaluate the educational use of LLMs.