Evidence map›Paper›PMID 42110446›Full record

ArticleFrontiers in medicine2026

Revolutionizing clinical skills training with Generative AI: the Intelligent Inquiry Training System framework.

Hongtao Liu, Huihui Cheng, Yun Zhang, Ying Huang, Danting Tan, Zheng Wang, Fan Zhang, Shaopeng Ming

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. 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

8 authors.

Hongtao Liu *School of Public Health and Health Management, Guangxi Health Science College, Nanning, China.
Huihui Cheng *Department of Anesthesiology, The East Hospital of Renmin Hospital of Wuhan University, Wuhan, China.
Yun ZhangNursing Teaching and Research Section, Guangxi Traditional Chinese Medical School, Nanning, China.
Ying HuangSchool of Public Health and Health Management, Guangxi Health Science College, Nanning, China.
Danting TanSchool of Public Health and Health Management, Guangxi Health Science College, Nanning, China.
Zheng WangSchool of Public Health and Health Management, Guangxi Health Science College, Nanning, China.
Fan ZhangDepartment of Anesthesiology, The East Hospital of Renmin Hospital of Wuhan University, Wuhan, China.
Shaopeng MingDepartment of Anesthesiology, Liuzhou Workers Hospital/The Fourth Affiliated Hospital of Guangxi Medical University, Key Laboratory of Anesthesia and Perioperative Neuroprotection of Liuzhou, Perioperative Neuroprotection Engineering Technology Research Center of Liuzhou, Liuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Practical training bridges theoretical knowledge and clinical skills in medical education but faces challenges such as resource scarcity, complex patient-physician relationship, and limited interactivity in traditional systems. Generative artificial intelligence (LLMs) offers innovative solutions for simulating virtual patients and enhancing communication training. Method: An Intelligent Inquiry Training System (IITS) was developed using Baidu's Wenxin LLM, featuring dynamic case generation, multimodal examination simulation, and emotional feedback modules. Integrated into a blended surgical curriculum, IITS employed pre-class problem-based learning (PBL) and post-class extended training. System efficacy was evaluated via student/instructor questionnaires. Results: IITS received software copyright certification and demonstrated cross-platform compatibility. Student feedback highlighted its auxiliary role, prompt responses, and need for improved diagnostic accuracy. Teachers praised its case generation efficiency and recommended AI-powered evaluation. Conclusion: IITS enhances clinical reasoning and communication skills through realistic virtual training, reducing teacher workload. Future iterations will integrate AI scoring and simulator mannequins to validate long-term impacts on clinical competency.

Indexed as

clinical skills traininggenerative artificial intelligenceIntelligent Inquiry Training Systemmedical educationvirtual patient simulation

Identifiers

PMID42110446
PMCPMC13154008

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.