Evidence map›Paper›PMID 39799356›Full record

Trial reportBMC medical education2025

Application of ChatGPT-assisted problem-based learning teaching method in clinical medical education.

Zeng Hui, Zhu Zewu, Hu Jiao, Cui Yu

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 7 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

38 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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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

4 authors.

Zeng HuiNursing Department, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410000, China.
Zhu ZewuDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, 410000, China.
Hu JiaoDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, 410000, China.
Cui YuDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, 410000, China. cuiyeyu@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionArtificial intelligence technology has a wide range of application prospects in the field of medical education. The aim of the study was to measure the effectiveness of ChatGPT-assisted problem-based learning (PBL) teaching for urology medical interns in comparison with traditional teaching.

methodsA cohort of urology interns was randomly assigned to two groups; one underwent ChatGPT-assisted PBL teaching, while the other received traditional teaching over a period of two weeks. Performance was assessed using theoretical knowledge exams and Mini-Clinical Evaluation Exercises. Students' acceptance and satisfaction with the AI-assisted method were evaluated through a survey.

resultsThe scores of the two groups of students who took exams three days after the course ended were significantly higher than their scores before the course. The scores of the PBL-ChatGPT assisted group were significantly higher than those of the traditional teaching group three days after the course ended. The PBL-ChatGPT group showed statistically significant improvements in medical interviewing skills, clinical judgment and overall clinical competence compared to the traditional teaching group. The students gave highly positive feedback on the PBL-ChatGPT teaching method.

conclusionThe study suggests that ChatGPT-assisted PBL teaching method can improve the results of theoretical knowledge assessment, and play an important role in improving clinical skills. However, further research is needed to examine the validity and reliability of the information provided by different chat AI systems, and its impact on a larger sample size.

Indexed as

Artificial IntelligenceInternship and ResidencyProblem-Based LearningUrologyAdultClinical CompetenceEducational MeasurementFemaleGenerative Artificial IntelligenceHumansMaleStudents, MedicalTeachingChatGPTMedical educationProblem-based learning

Identifiers

PMID39799356
PMCPMC11724493

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.