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

Artificial 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. This 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. Among 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. Our 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.

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Publication Details

Journal
BMC Medical Education
Published
2026-04-22
DOI
https://doi.org/10.1186/s12909-026-09084-z
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
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article

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

Wenyi Gan, Yiyi Zhang, Quan Chen, Mingjia Xiao et al.
BMC Medical Education
Artificial Intelligence in Healthcare and Education
article

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

Wenyi Gan, Yiyi Zhang, Quan Chen, Mingjia Xiao, Guangdi Chu, Aimin Jiang, Qi Chang, Yukang Liu, Bufu Tang, Weiming Mou, Jiarui Xie, Zaoqu Liu, Peng Luo, Lingxuan Zhu, Dongqiang Zeng, Anqi Lin
article en

Abstract

Artificial 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. This 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. Among 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. Our 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.

BMC Medical Education
Qingdao University (CN), Central South University (CN), Sun Yat-sen University (CN), Second Military Medical University (CN), Shanghai Jiao Tong University (CN), South China Normal University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Fudan University (CN), Quzhou City People's Hospital (CN), Sixth Affiliated Hospital of Sun Yat-sen University (CN), Nanfang Hospital (CN), Zhuhai People's Hospital (CN), Changhai Hospital (CN), Affiliated Hospital of Qingdao University (CN), Zhujiang Hospital (CN), Quzhou University (CN), Zhongshan Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Shanghai First People's Hospital (CN), Xiangya Hospital Central South University (CN), First Affiliated Hospital of Jinan University (CN), Southern Medical University (CN), University of Hong Kong (HK), South China University of Technology (CN)
Quality Education
Openalex Percentile: Top 16%
Artificial Intelligence in Healthcare and Education
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