BMC medical education · 2026
Utilizing generative artificial intelligence to create simulated patient for history taking in gynecology.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод в очереди
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Gao Lingling, Zhang Li, Zhang Yanxin, Wu Min, Xu Jianbo, Lu Dan
Аннотация
INTRODUCTION: This study aimed to evaluate the effectiveness of a generative artificial intelligence based simulated patient model in improving gynecological history-taking skills among medical students, compared with traditional clinical teaching methods.
METHODS: A prospective randomized controlled trial was conducted involving 40 fourth-year medical students during their gynecology clerkship. Students were randomly assigned to either an AI group (n = 20) or a control group (n = 20). Both groups received identical theoretical instruction on history taking. The control group practiced interviews with instructor-led standardized patients, while the AI group conducted simulated interviews with a large language model configured to behave as a real patient using predefined gynecological cases. Outcomes were assessed using a gynecological history-taking content checklist, Objective Structured Clinical Examination (OSCE) Score, and the Mini-Clinical Evaluation Exercise (Mini-CEX). Statistical analyses were performed using t-tests, chi-square tests, or Mann-Whitney U tests.
RESULTS: Baseline characteristics did not differ significantly between the two groups. Students in the AI group achieved a significantly higher history-taking checklist completion rate than those in the control group and missed fewer key history items. The AI group achieved significantly higher total OSCE scores compared to the control group. Mini-CEX results showed that the AI group performed significantly better in medical interviewing skills and organization/efficiency. No significant differences were observed in professionalism, clinical judgment, counseling skills, or overall clinical competence.
CONCLUSIONS: The use of a generative AI-simulated patient significantly enhanced the information completeness and organizational quality of gynecological history taking among medical students. AI-based simulation appears to be an effective and scalable adjunct to traditional standardized patients, offering educational benefits for reinforcing systematic clinical inquiry. However, these findings are preliminary as a single-center study with a limited sample size. Further large-scale, multi-center research is required to confirm the generalizability of these results across diverse educational settings.
METHODS: A prospective randomized controlled trial was conducted involving 40 fourth-year medical students during their gynecology clerkship. Students were randomly assigned to either an AI group (n = 20) or a control group (n = 20). Both groups received identical theoretical instruction on history taking. The control group practiced interviews with instructor-led standardized patients, while the AI group conducted simulated interviews with a large language model configured to behave as a real patient using predefined gynecological cases. Outcomes were assessed using a gynecological history-taking content checklist, Objective Structured Clinical Examination (OSCE) Score, and the Mini-Clinical Evaluation Exercise (Mini-CEX). Statistical analyses were performed using t-tests, chi-square tests, or Mann-Whitney U tests.
RESULTS: Baseline characteristics did not differ significantly between the two groups. Students in the AI group achieved a significantly higher history-taking checklist completion rate than those in the control group and missed fewer key history items. The AI group achieved significantly higher total OSCE scores compared to the control group. Mini-CEX results showed that the AI group performed significantly better in medical interviewing skills and organization/efficiency. No significant differences were observed in professionalism, clinical judgment, counseling skills, or overall clinical competence.
CONCLUSIONS: The use of a generative AI-simulated patient significantly enhanced the information completeness and organizational quality of gynecological history taking among medical students. AI-based simulation appears to be an effective and scalable adjunct to traditional standardized patients, offering educational benefits for reinforcing systematic clinical inquiry. However, these findings are preliminary as a single-center study with a limited sample size. Further large-scale, multi-center research is required to confirm the generalizability of these results across diverse educational settings.