Evidence map›Paper›PMID 42200070›Full record

ArticleFrontiers in medicine2026

Virtual patients and standardized patients combined training is associated with improved clinical reasoning among medical students.

Tao Shen, Ruitao Zhang, Heng Wang, Dan Li, JiangLi Han

Abstract read
In one paragraph

Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Tao Shen *NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Department of Cardiology, Peking University Third Hospital, Haidian, China.
Ruitao Zhang *NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Department of Cardiology, Peking University Third Hospital, Haidian, China.
Heng WangDepartment of Education, Peking University Third Hospital, Haidian, China.
Dan LiNHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Department of Cardiology, Peking University Third Hospital, Haidian, China.
JiangLi HanNHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Department of Cardiology, Peking University Third Hospital, Haidian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To develop an artificial intelligence (AI)-driven virtual standardized patients (VSPs) system, and to evaluate its educational effectiveness when combined with traditional standardized patients (SPs) training. Methods: Leveraging natural language processing and a Chinese large language model, we built an AI-powered VSPs application. A total of 80 medical students at Peking University Third Hospital were randomized into two groups: experimental ( Results: The experimental group showed greater improvement than the control group in clinical reasoning scores (1.3 ± 0.7 vs. 0.3 ± 0.5, 95% CI: 0.73-1.27, Conclusion: Combined VSPs and SPs training showed better performance than the SPs-only approach used in this study.

Indexed as

artificial intelligenceclinical reasoningmedical educationstandardized patientsvirtual standardized patients

Identifiers

PMID42200070
PMCPMC13199084

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.