ArticleJMIR formative research2025
The Validity of Generative Artificial Intelligence in Evaluating Medical Students in Objective Structured Clinical Examination: Experimental Study.
Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in urological education: a systematic review.BMC medical education · 2026Pooled it
- The impact of generative artificial intelligence tools on assessment equity for non-native English-speaking medical students: a systematic review.BMC medical education · 2026Pooled it
- Agreement Between Reasoning-Oriented Generative AI Models and Clinical Educators in Evaluating Japanese Objective Structured Clinical Examination Transcripts: Preliminary Comparative Study.JMIR formative research · 2026Article
- Artificial intelligence analysis of facial movements and satisfaction in online medical lectures: a cross-sectional study.BMC medical education · 2026Article
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7 authors.
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Abstract
backgroundThe Objective Structured Clinical Examination (OSCE) has been widely used to evaluate students in medical education. However, it is resource-intensive, presenting challenges in implementation. We hypothesized that generative artificial intelligence (AI) such as ChatGPT-4 could serve as a complementary assessor and alleviate the burden of physicians in evaluating OSCE.
objectiveBy comparing the evaluation scores between generative AI and physicians, this study aims to evaluate the validity of generative AI as a complementary assessor for OSCE.
methodsThis experimental study was conducted at a medical university in Japan. We recruited 11 fifth-year medical students during the general internal medicine clerkship from April 2023 to December 2023. Participants conducted a mock medical interview with a patient experiencing abdominal pain and wrote patient notes. Four physicians independently evaluated the participants by reviewing medical interview videos and patient notes, while ChatGPT-4 was provided with interview transcripts and notes. Evaluations were conducted using the 6-domain rubric (patient care and communication, history taking, physical examination, patient notes, clinical reasoning, and management). Each domain was scored using a 6-point Likert scale, ranging from 1 (very poor) to 6 (excellent). Median scores were compared using the Wilcoxon signed-rank test, and the agreement between ChatGPT-4 and physicians was assessed using intraclass correlation coefficients (ICCs). All P values <.05 were considered statistically significant.
resultsAlthough ChatGPT-4 assigned higher scores than physicians in terms of physical examination (median 4.0, IQR 4.0-5.0 vs median 4.0, IQR 3.0-4.0; P=.02), patient notes (median 6.0, IQR 5.0-6.0 vs median 4.0, IQR 4.0-4.0; P=.002), clinical reasoning (median 5.0, IQR 5.0-5.0 vs median 4.0, IQR 3.0-4.0; P<.001), and management (median 6.0, IQR 5.0-6.0 vs median 4.0, IQR 2.5-4.5; P=.002), there were no significant differences in the scores of patient care and communication (median 5.0, IQR 5.0-5.0 vs median 5.0, IQR 4.0-5.0; P=.06) and history taking (median 5.0, IQR 4.0-5.0 vs median 5.0, IQR 4.0-5.0; P>.99), respectively. ICC values were low in all domains, except for history taking, where the agreement was still poor (ICC=0.36, 95% CI -0.32 to 0.78).
conclusionsChatGPT-4 produced higher evaluation scores than physicians in several OSCE domains, though the agreement between them was poor. Although these preliminary results suggest that generative AI may be able to support assessment in some domains of OSCE, further research is needed to establish its reproducibility and validity. Generative AI like ChatGPT-4 shows potential as a complementary assessor for OSCE.
trial registrationUniversity Hospital Medical Information Network Clinical Trials Registry UMIN000050489; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_his_list.cgi?recptno=R000057513.
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