Evidence map›Paper›PMID 42786480›Full record

Trial reportBMC medical education2026

Application of an AI-enabled personalized medical education model for improving clinical thinking among cardiovascular surgery trainees.

Yuehang Yang, Li Sheng, Liang Huang, Qiannan Guo, Yuting Hu, Jiawei Shi

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

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

6 authors.

Yuehang Yang *Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei, China.
Li Sheng *Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei, China.
Liang HuangDepartment of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei, China.
Qiannan GuoDepartment of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei, China.
Yuting HuDepartment of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei, China. huyuting66@qq.com.
Jiawei ShiDepartment of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei, China. 15807298713@163.com.ORCID https://orcid.org/0009-0008-3999-4598

Funding

National Key Research and Development Program of China 2023YFC2706205
6 · The paper itself

Abstract

objectiveCardiovascular surgery involves complex clinical scenarios and high demands on clinical judgment. This study aimed to evaluate the effectiveness of an artificial intelligence (AI)-enabled personalized teaching model in cardiovascular surgery training.

methodsFrom February to June 2025, 158 trainees in the department of cardiovascular surgery at our hospital were randomly assigned to an experimental group (n = 79) or a control group (n = 79). The control group received conventional teaching, including lectures, bedside teaching, and case discussions. The experimental group received a personalized teaching model integrating an intelligent question bank, staged case-based teaching, and AI-based standardized patient training. Outcomes included theoretical examination scores, the Chinese version of the California Critical Thinking Disposition Inventory (CTDI-CV), clinical interview performance, and teaching satisfaction.

resultsThe experimental group achieved significantly higher theoretical examination scores than the control group (84.56 ± 5.07 vs. 77.95 ± 9.04, P < 0.001). CTDI-CV results showed significantly higher total scores and higher scores in truth-seeking, open-mindedness, analyticity, systematicity, and inquisitiveness in the experimental group (all P < 0.05). In the clinical interview assessment, the experimental group scored significantly higher in completeness of information collection, communication skills, diagnostic reasoning, and overall performance (all P < 0.001), while the between-group difference in empathy was not significant (P = 0.328). Teaching satisfaction in the experimental group was high, with an overall satisfaction score of 4.68 ± 0.39.

conclusionThe AI-enabled personalized teaching model may represent a feasible and effective approach for cardiovascular surgery education. It was associated with higher theoretical examination scores, better critical thinking disposition, higher scores in several dimensions of simulated clinical interview performance, and high learner satisfaction. Further studies are needed to confirm its broader applicability and long-term educational value.

Indexed as

Artificial IntelligenceCardiovascular Surgical ProceduresClinical ReasoningModels, EducationalAdultClinical CompetenceFemaleHumansMaleThinkingArtificial intelligenceCardiovascular surgeryClinical thinkingLarge language modelMedical education

Identifiers

PMID42786480
PMCPMC13602554

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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.