Evidence map›Paper›PMID 41851693›Full record

ArticleBMC medical education2026

Adoption of generative AI chatbots among medical postgraduates at two universities in China: patterns, attitudes, and concerns.

Lijun Zhao, Qianqian Han, Peijuan Li, Hua Dai, Xuegui Ju

Abstract read
In one paragraph

Article in BMC medical education, 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. 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

5 authors.

Lijun ZhaoDepartment of General Practice, General Practice Medical Center, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, Sichuan Province, 610041, China.
Qianqian HanDepartment of Pathology, West China Hospital of Sichuan University, Chengdu, 610041, China.
Peijuan LiDepartment of General Practice, General Practice Medical Center, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, Sichuan Province, 610041, China.
Hua DaiDepartment of General Practice, General Practice Medical Center, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, Sichuan Province, 610041, China. 19541373@qq.com.
Xuegui JuThe First Affiliated Hospital of Chengdu Medical College, No. 278 Middle of Baoguang Road, Xindu District, Chengdu, Sichuan, 610500, China. juxuegui@outlook.com.

Funding

Chengdu Medical College, Education Reform Research Project JG201905Chengdu Science and Technology Bureau, Technological Innovation and Research & Development Project 2024-YF05-00409-SNScience & Technology Department of Sichuan Province, Science and Technology Training Program, Industry Science Popularization Capacity Enhancement Project 2025JDKP0031Sichuan University Higher Education Teaching Reform Project (Phase XI) SCU11144West China Hospital, Sichuan University, 1·3·5 project for disciplines of excellence-Clinical Research Fund 2024HXFH020
6 · The paper itself

Abstract

backgroundGenerative artificial intelligence (AI) chatbots are gaining attention in medical education globally for their potential to support academic writing, clinical reasoning, and personalized learning. However, little is known about their adoption, benefits, and concerns among Chinese postgraduate medical students, particularly across distinct clinical- and academic-track programs that characterize China’s unique medical education system.

methodsA cross-sectional survey was conducted among 340 postgraduate medical students from two universities in Chengdu, China. A structured questionnaire assessed AI awareness, usage patterns, perceived benefits, attitudes, and concerns. Descriptive statistics, subgroup analyses, and Pearson correlation analyses were applied to examine differences by gender and degree type.

resultsMost students (82.9%) reported strong AI awareness, with DeepSeek (90.9%) and ChatGPT (55.2%) most frequently used. Common applications included literature review (61.5%), exam preparation (55.0%), and clinical case analysis (48.5%). Reported benefits encompassed faster information retrieval (70.8%) and improved writing precision (64.3%). Overall satisfaction was high (mean 4.4/5), and 84.0% supported curriculum integration. Female students use AI more frequently but expressed greater concerns, clinical-track students demonstrated higher awareness than academic-track students. Awareness, usage, and attitudes showed positive correlations, while concerns about accuracy and ethics remained independent of prior exposure.

conclusionsThis study at two universities in Chengdu demonstrates widespread adoption and favorable perceptions of generative AI chatbots among postgraduate medical students. Findings suggest that structured curricular integration, accompanied by ethical safeguards addressing China-specific challenges, may maximize benefits while mitigating risks. Multi-center studies are needed to validate these regional patterns nationally.

Indexed as

Attitude of Health PersonnelEducation, Medical, GraduateGenerative Artificial IntelligenceStudents, MedicalAdultChinaCross-Sectional StudiesFemaleHumansMaleSurveys and QuestionnairesUniversitiesYoung AdultChatbotsChinaGenerative artificial intelligenceMedical educationPerceptionsPostgraduate medical students

Identifiers

PMID41851693
PMCPMC13085431

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.