Evidence map›Paper›PMID 40866973›Full record

SynthesisBMC medical education2025

Effectiveness of AI-assisted medical education for Chinese undergraduate medical students: a meta-analysis.

Jingyue Peng, Hongying Zhang, Xiaohua Tu, Xuemei Zhang, Qiuhan Wu, Yijun Wang, Deng Xiao

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis 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 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  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.

Jingyue PengDepartment of Rehabilitation Medicine, The Affiliated Rehabilitation Hospital of Chongqing Medical University, No. 50 Xiexiao Road, Chongqing, 400050, China.
Hongying ZhangDepartment of Science and Education, The Affiliated Rehabilitation Hospital of Chongqing Medical University, No. 50 Xiexiao Road, Chongqing, 400050, China.
Xiaohua TuDepartment of Rehabilitation Medicine, The Affiliated Rehabilitation Hospital of Chongqing Medical University, No. 50 Xiexiao Road, Chongqing, 400050, China.
Xuemei ZhangExperimental Teaching Management Center, Chongqing Medical University, No. 1 Yixueyuan Road, Chongqing, 400016, China.
Qiuhan WuDepartment of Rehabilitation Medicine, The Affiliated Rehabilitation Hospital of Chongqing Medical University, No. 50 Xiexiao Road, Chongqing, 400050, China.
Yijun WangDepartment of Rehabilitation Medicine, The Affiliated Rehabilitation Hospital of Chongqing Medical University, No. 50 Xiexiao Road, Chongqing, 400050, China.
Deng XiaoDepartment of Rehabilitation Medicine, The Affiliated Rehabilitation Hospital of Chongqing Medical University, No. 50 Xiexiao Road, Chongqing, 400050, China. 151686484@qq.com.

Funding

This study was supported by the Project of Incubation Fund of the Affiliated Rehabilitation Hospital of Chongqing Medical University in 2022 RHCQMU2022005
6 · The paper itself

Abstract

backgroundIn recent years, artificial intelligence (AI) has been adopted as an innovative teaching method. However, no studies have comprehensively evaluated the effectiveness of AI in the context of medical education in China. This study aimed to assess the effectiveness of AI-assisted teaching methods versus traditional teaching methods regarding their impact on medical education outcomes in China.

methodsWe followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and retrieved studies published in the Web of Science, PubMed, EMBASE, the Cochrane Library, the Chinese National Knowledge Infrastructure, VIP Database and the Chinese Wanfang Database from January 2015 to December 2024. The standardized mean difference (SMD) with a 95% confidence interval (CI) was calculated, and heterogeneity was evaluated using I² statistics, with subsequent meta-regression analysis employed to identify the contributing factors.

resultsTwelve studies involving 824 medical students were included. All studies provided usable data on examination scores. The pooled analysis revealed a significant difference in favor of AI-assisted teaching compared with traditional teaching methods (SMD = 2.06, 95% CI: 1.35–2.76). In addition, AI-assisted teaching significantly increased student satisfaction in eight studies (OR = 5.80, 95% CI: 3.30–10.18). Meta-regression analysis indicated that randomization, study duration and type of AI technologies were the primary factors contributing to heterogeneity.

conclusionThe AI-assisted approach to medical education in China was shown to be more effective than traditional teaching methods in improving examination scores and student satisfaction, offering substantial evidence for the adoption of AI in medical education. These results highlighted the potential advantages of incorporating AI into medical teaching practices. Future research should focus on investigating the effectiveness of AI-assisted teaching across diverse educational systems and applications.

Indexed as

Artificial IntelligenceEducation, Medical, UndergraduateStudents, MedicalChinaEducational MeasurementHumansTeachingArtificial intelligenceChinaMedical educationMeta-analysis

Identifiers

PMID40866973
PMCPMC12382127

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.