Evidence map›Paper›PMID 41343812›Full record

ArticleJMIR formative research2025

The Validity of Generative Artificial Intelligence in Evaluating Medical Students in Objective Structured Clinical Examination: Experimental Study.

Masashi Yokose, Takanobu Hirosawa, Tetsu Sakamoto, Ren Kawamura, Yudai Suzuki, Yukinori Harada, Taro Shimizu

Abstract read
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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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Masashi YokoseDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan.ORCID 0000-0002-2685-0658
Takanobu HirosawaDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan.ORCID 0000-0002-3573-8203
Tetsu SakamotoDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan.ORCID 0000-0001-9104-8891
Ren KawamuraDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan.ORCID 0000-0002-5632-3218
Yudai SuzukiDepartment of Internal Medicine, Yamagata Prefectural Kahoku Hospital, Yamagata, Japan.ORCID 0000-0002-7462-9878
Yukinori HaradaDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan.ORCID 0000-0001-6042-7397
Taro ShimizuDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan.ORCID 0000-0002-3788-487X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceClinical CompetenceEducational MeasurementStudents, MedicalFemaleGenerative Artificial IntelligenceHumansJapanMaleReproducibility of ResultsChatGPT-4generative artificial intelligencemedical educationmedical studentsobjective structured clinical examinationOSCE

Identifiers

PMID41343812
PMCPMC12715467

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.