Enhancing Medical Interview Skills Through AI-Simulated Patient Interactions: Nonrandomized Controlled Trial.

Autor:	Yamamoto A; Department of Hematology and Oncology, Okayama University Hospital, Okayama, Japan, Okayama, Japan., Koda M; Co-learning Community Healthcare Re-innovation Office, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan, Okayama, Japan., Ogawa H; Department of Primary Care and Medical Education, Dentistry and Pharmaceutical Sciences, Okayama University Graduate School of Medicine, Okayama, Japan, Okayama, Japan.; Department of General Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan, Okayama, Japan., Miyoshi T; Department of General Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan, Okayama, Japan.; Center for Education in Medicine and Health Sciences, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan, Okayama, Japan., Maeda Y; Department of Hematology and Oncology, Okayama University Hospital, Okayama, Japan, Okayama, Japan., Otsuka F; Department of General Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan, Okayama, Japan., Ino H; Center for Education in Medicine and Health Sciences, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan, Okayama, Japan.
Jazyk:	angličtina
Zdroj:	JMIR medical education [JMIR Med Educ] 2024 Sep 23; Vol. 10, pp. e58753. Date of Electronic Publication: 2024 Sep 23.
DOI:	10.2196/58753
Abstrakt:	Background: Medical interviewing is a critical skill in clinical practice, yet opportunities for practical training are limited in Japanese medical schools, necessitating urgent measures. Given advancements in artificial intelligence (AI) technology, its application in the medical field is expanding. However, reports on its application in medical interviews in medical education are scarce. Objective: This study aimed to investigate whether medical students' interview skills could be improved by engaging with AI-simulated patients using large language models, including the provision of feedback. Methods: This nonrandomized controlled trial was conducted with fourth-year medical students in Japan. A simulation program using large language models was provided to 35 students in the intervention group in 2023, while 110 students from 2022 who did not participate in the intervention were selected as the control group. The primary outcome was the score on the Pre-Clinical Clerkship Objective Structured Clinical Examination (pre-CC OSCE), a national standardized clinical skills examination, in medical interviewing. Secondary outcomes included surveys such as the Simulation-Based Training Quality Assurance Tool (SBT-QA10), administered at the start and end of the study. Results: The AI intervention group showed significantly higher scores on medical interviews than the control group (AI group vs control group: mean 28.1, SD 1.6 vs 27.1, SD 2.2; P=.01). There was a trend of inverse correlation between the SBT-QA10 and pre-CC OSCE scores (regression coefficient -2.0 to -2.1). No significant safety concerns were observed. Conclusions: Education through medical interviews using AI-simulated patients has demonstrated safety and a certain level of educational effectiveness. However, at present, the educational effects of this platform on nonverbal communication skills are limited, suggesting that it should be used as a supplementary tool to traditional simulation education. (©Akira Yamamoto, Masahide Koda, Hiroko Ogawa, Tomoko Miyoshi, Yoshinobu Maeda, Fumio Otsuka, Hideo Ino. Originally published in JMIR Medical Education (https://mededu.jmir.org), 23.09.2024.)
Databáze:	MEDLINE
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