Evidence map›Paper›PMID 42700004›Full record

ArticleMedicine2026

A comparative evaluation of ChatGPT-assisted and traditional methods of teaching diagnostic assessments to medical students: A randomized controlled study.

Hua Xu, Yu Sun, Zhi-Wen Mo, Yu-Nan Man, San-Mao Liu, Yu-Ning Wei, Mao-Lin He

Abstract readComparative StudyEvaluation Study
In one paragraph

Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Hua XuCenter for Education Evaluation & Faculty, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, PR China.
Yu SunDivision of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, PR China.
Zhi-Wen MoCenter for Education Evaluation & Faculty, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, PR China.
Yu-Nan ManDivision of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, PR China.
San-Mao LiuDivision of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, PR China.
Yu-Ning WeiSchool of Humanities and Management, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, PR China.
Mao-Lin HeDivision of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, PR China.ORCID 0000-0002-3563-763

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid development of artificial intelligence (AI) technology has brought new opportunities and challenges to the ever-evolving field of medical education. However, the specific impact of AI on the teaching of diagnostic assessments to medical students requires further investigation. The purpose of this study was to evaluate the effectiveness of ChatGPT-assisted teaching of diagnoses. Eighty medical students were randomly divided into 2 groups; 1 group received ChatGPT-assisted training, and the other group received conventional instruction. We evaluated the effectiveness of both teaching methods using a theoretical examination and the Mini-Clinical Evaluation Exercise (Mini-CEX). We also assessed satisfaction with the ChatGPT-assisted teaching method using a structured survey. The ChatGPT-assisted students scored significantly higher on the theoretical knowledge examination compared to the traditional teaching group (t = 6.34, P < .001). The ChatGPT-assisted group showed significant improvement on the Mini-CEX examination, particularly on medical history taking (Χ2 = 5.115, P = .024), doctor-patient communication (Χ2 = 5.051, P = .025), professionalism (Χ2 = 4.073, P = .044), clinical judgment (Χ2 = 4.503, P = .034), and overall capabilities (Χ2 = 7.218, P = .007). The majority of participants rated the ChatGPT teaching method highly and provided positive feedback, highlighting the benefits of interactive learning and skill acquisition. ChatGPT-assisted teaching not only improved the students' scores on the theoretical knowledge of diagnostic assessments, but also enhanced the clinical knowledge of the medical students. The positive feedback of the ChatGPT-assisted group shows that this method has the potential to supplement and enhance conventional teaching methods in medical education. These results support further expansion and the application of AI in medical education.

Indexed as

Education, Medical, UndergraduateStudents, MedicalAdultArtificial IntelligenceClinical CompetenceEducational MeasurementFemaleGenerative Artificial IntelligenceHumansMaleRandom AllocationTeachingChatGPTdiagnosticmedical educationMini-Clinical Evaluation Exercise

Identifiers

PMID42700004
PMCPMC13549642

What OpenQuestion holds

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Registered trials

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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.