Evidence map›Paper›PMID 42021208›Full record

ArticleBMC medical education2026

Deciphering AI-chatbots utilization in clinical practice, research and education among Chinese medical students in generative AI era: a cross-sectional study.

Wenyi Gan, Yukang Liu, Anqi Lin, Bufu Tang, Aimin Jiang, Chang Qi, Lingxuan Zhu, Weiming Mou, Dongqiang Zeng, Mingjia Xiao and 6 more

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. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

16 authors.

Wenyi Gan *Department of Joint Surgery and Sports Medicine, Zhuhai People's Hospital (The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan University), Guangdong, China.
Yukang Liu *Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China.
Anqi Lin *Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China.
Bufu Tang *Department of Radiation Oncology, Zhongshan Hospital Affiliated to Fudan University, Shanghai, China.
Aimin JiangDepartment of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China.
Chang QiThe University of Hong Kong, Hong Kong SAR, China.
Lingxuan ZhuDepartment of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China.
Weiming MouDepartment of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Dongqiang ZengDepartment of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Mingjia XiaoHepatobiliary Surgery Department, Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China.
Guangdi ChuDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Zaoqu LiuInstitute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Jiarui XieDepartment of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China.
Quan ChenDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Yiyi ZhangSchool of Education, South China Normal University, Guangzhou, 510631, China.
Peng LuoDepartment of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China. luopeng@smu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence chatbots (AI-chatbots) are increasingly being applied in the medical field; however, their specific utilization in Chinese medical education and practice remains poorly understood. This study aims to systematically analyze the perception and utilization of AI-chatbots among Chinese medical students, explore potential factors influencing their effectiveness, and provide recommendations for policy development and technological optimization.

methodsThis cross-sectional study utilized an electronic questionnaire distributed through multiple channels to collect data from Chinese medical students, with most participants recruited from Guangdong Province, Southern China. The questionnaire assessed demographic characteristics, AI-chatbot usage patterns, attitudes towards their application in clinical, research, and educational domains, and perspectives on potential issues. Data were analyzed using descriptive statistics, independent sample t-tests, chi-square tests, Wilcoxon rank-sum tests, Logistic regressions and Pearson correlation analyses.

resultsAmong 414 respondents, 53.6% had used AI-chatbots, primarily ChatGPT (58%). AI-chatbots were favored for research (36.5%) and education (40.1%), with limited use in clinical practice (4.5%). Users' evaluations of AI-chatbots in research and education were significantly more positive than those of non-users. Use frequency, time, and attention to updates demonstrated significant positive correlations with positive responses (P < 0.05). Information accuracy emerged as a top concern, with over 65% of respondents indicating the urgency for a solution.

conclusionOur findings indicate that AI-chatbots are widely utilized particularly in research and educational-related domains among Chinese medical students, with our sample most recruited from Southern China. However, the implementation of AI-chatbots in clinical practice continues to face significant challenges. Medical students exhibit a cautiously optimistic attitude towards AI-chatbots, acknowledging their potential while remaining cognizant of associated risks. Future research should prioritize investigating the safety and efficacy of AI-chatbots in clinical practice, as well as exploring optimal strategies for their integration into the medical education curriculum.

Indexed as

Artificial IntelligenceEducation, MedicalStudents, MedicalAdultAttitude of Health PersonnelChinaCross-Sectional StudiesEast Asian PeopleFemaleGenerative Artificial IntelligenceHumansMaleSurveys and QuestionnairesYoung AdultChatbotClinical practiceEducationGenerative AIResearch

Identifiers

PMID42021208
PMCPMC13235026

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.