Knowledge and Awareness of Generative Artificial Intelligence Use in Medicine Among International Stakeholders: A Cross-Sectional Study

Xufei Luo; Bingyi Wang; Yule Li; Shuang Liu; Haodong Li; Yaxuan Ren; Wah Yang; Kyle Lam; Stephen R Ali; Gemma Sharp; Fabio Ynoe Moraes; Ye Wang; Di Zhu; Zhenhua Yang; Daher Mohammad; Robert Fruscio; Maged N. Kamel Boulos; Zhicheng Lin; Kazuki Ide; Xuping Song; Lu Zhang; Yih Chung Tham; Hui Liu; Long Ge; Yaolong Chen; Zhaoxiang Bian

doi:10.1111/jebm.70034

Journal of Evidence-Based Medicine ›› 2025, Vol. 18 ›› Issue (2) : e70034 DOI: 10.1111/jebm.70034

ARTICLE

Knowledge and Awareness of Generative Artificial Intelligence Use in Medicine Among International Stakeholders: A Cross-Sectional Study

Xufei Luo ¹^,²^,³^,⁴^,⁵
, Bingyi Wang ¹^,²^,³^,⁴^,⁵
, Yule Li ⁶
, Shuang Liu ⁷^,⁸
, Haodong Li ⁹
, Yaxuan Ren ¹⁰
, Wah Yang ¹¹
, Kyle Lam ¹²
, Stephen R Ali ¹³
, Gemma Sharp ¹⁴
, Fabio Ynoe Moraes ¹⁵
, Ye Wang ⁹
, Di Zhu ⁹
, Zhenhua Yang ¹⁶
, Daher Mohammad ¹⁷
, Robert Fruscio ¹⁸^,¹⁹
, Maged N. Kamel Boulos ²⁰
, Zhicheng Lin ²¹^,²²
, Kazuki Ide ²³^,²⁴^,²⁵
, Xuping Song ²⁶
, Lu Zhang ²⁷
, Yih Chung Tham ²⁸^,²⁹^,³⁰^,³¹
, Hui Liu ¹^,²^,³^,⁴^,⁵
, Long Ge ³²
, Yaolong Chen ¹^,²^,³^,⁴^,⁵
, Zhaoxiang Bian ¹⁶^,³³

Author information +

¹. Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China

². Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China

³. World Health Organization Collaboration Center for Guideline Implementation and Knowledge Translation, Lanzhou, China

⁴. Institute of Health Data Science, Lanzhou University, Lanzhou, China

⁵. Key Laboratory of Evidence Based Medicine of Gansu Province, Lanzhou University, Lanzhou, China

⁶. Editorial Office of Medical Journal of Peking Union Medical College Hospital, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China

⁷. Chinese Journal of Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China

⁸. Tianjin Institutes of Health Science, Tianjin, China

⁹. School of Public Health, Lanzhou University, Lanzhou, China

¹⁰. School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China

¹¹. Department of Metabolic and Bariatric Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China

¹². Department of Surgery and Cancer, Imperial College London, London, UK

¹³. Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, UK

¹⁴. School of Psychology, University of Queensland, Brisbane, Australia

¹⁵. Department of Oncology, Queen's University, Kingston, Canada

¹⁶. School of Chinese Medicine, Vincent V.C. Woo Chinese Medicine Clinical Research Institute, Hong Kong Baptist University, Hong Kong, China

¹⁷. Orthopedic Department, Hôtel Dieu de France, Beirut, Lebanon

¹⁸. UO Gynecology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy

¹⁹. Department of Medicine and Surgery, University of Milan-Bicocca, Milan, Italy

²⁰. School of Medicine, University of Lisbon, Lisbon, Portugal

²¹. Department of Psychology, Yonsei University, Seoul, Republic of Korea

²². Department of Psychology, University of Science and Technology of China, Hefei, China

²³. Division of Scientific Information and Public Policy, Center for Infectious Disease Education and Research (CIDER), The University of Osaka, Osaka, Japan

²⁴. Research Center on Ethical, Legal and Social Issues, The University of Osaka, Osaka, Japan

²⁵. Division of Scientific Information and Public Policy, Center for Infectious Disease Education and Research (CiDER), The University of Osaka, Osaka, Japan

²⁶. Department of Social Medicine and Health Management, Evidence-Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou, China

²⁷. Department of Computer Science, Hong Kong Baptist University, Hongkong, China

²⁸. Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

²⁹. Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

³⁰. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore

³¹. Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore

³². Department of Health Policy and Management, School of Public Health, Lanzhou University, Lanzhou, Gansu, China

³³. Chinese EQUATOR Centre, Hong Kong, China

chevidence@lzu.edu.cn

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Abstract

Objective: To assess the knowledge, attitudes, and practices (KAP) of medical stakeholders regarding the use of generative artificial intelligence (GAI) tools.

Methods: A cross-sectional survey was conducted among stakeholders in medicine. Participants included researchers, clinicians, and medical journal editors with varying degrees of familiarity with GAI tools. The survey questionnaire comprised 40 questions covering four main dimensions: basic information, knowledge, attitudes, and practices related to GAI tools. Descriptive analysis, Pearson's correlation, and multivariable regression were used to analyze the data.

Results: The overall awareness rate of GAI tools was 93.3%. Participants demonstrated moderate knowledge (mean score 17.71 ± 5.56), positive attitudes (mean score 73.32 ± 15.83), and reasonable practices (mean score 40.70 ± 12.86). Factors influencing knowledge included education level, geographic region, and attitudes (p < 0.05). Attitudes were influenced by work experience and knowledge (p < 0.05), while practices were driven by both knowledge and attitudes (p < 0.001). Participants from outside China scored higher in all dimensions compared to those from China (p < 0.001). Additionally, 74.0% of participants emphasized the importance of reporting GAI usage in research, and 73.9% advocated for naming the specific tool used.

Conclusion: The findings highlight a growing awareness and generally positive attitude toward GAI tools among medical stakeholders, alongside the recognition of their ethical implications and the necessity for standardized reporting practices. Targeted training and the development of clear reporting guidelines are recommended to enhance the effective use of GAI tools in medical research and practice.

Keywords

ChatGPT / cross-sectional survey / generative artificial intelligence / knowledge and awareness

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Xufei Luo, Bingyi Wang, Yule Li, Shuang Liu, Haodong Li, Yaxuan Ren, Wah Yang, Kyle Lam, Stephen R Ali, Gemma Sharp, Fabio Ynoe Moraes, Ye Wang, Di Zhu, Zhenhua Yang, Daher Mohammad, Robert Fruscio, Maged N. Kamel Boulos, Zhicheng Lin, Kazuki Ide, Xuping Song, Lu Zhang, Yih Chung Tham, Hui Liu, Long Ge, Yaolong Chen, Zhaoxiang Bian. Knowledge and Awareness of Generative Artificial Intelligence Use in Medicine Among International Stakeholders: A Cross-Sectional Study. Journal of Evidence-Based Medicine, 2025, 18(2): e70034 DOI:10.1111/jebm.70034