Analysis of doctors willingness to use medical artificial intelligence and influencing factors

Corresponding author: Li Ming, ming-li18@mails.tsinghua.edu.cn

DOI: 10.12201/bmr.202312.00020

•  

  Abstract: Objective/Significance: To explore the attitudes, willingness, and influencing factors of Chinese doctors towards medical artificial intelligence. Method/Process: A cross-sectional survey was conducted by distributing closed-ended questionnaires via WeChat to 327 doctors. The questionnaire content included the doctors background, their understanding of AI, their level of acceptance, and their willingness to use it. Descriptive statistics, intergroup comparison, and logistic regression analysis were used. Results/Conclusion: In terms of trust in AI, 4 people (1.22%) had high trust, 83 people (25.38%) had moderate trust, 219 people (66.97%) had general trust, 19 people (5.81%) had low trust, and 2 people (0.61%) had very low trust. In terms of willingness to use, 170 people (51.99%) were proactive in using it and 81（24.77%） were reactive. At the same time, there were significant differences between groups in terms of gender (P=0.017), education level (P=0.045), doctors attention to AI (P=0.000), and the number of mobile apps (P=0.000). Most doctors have a positive attitude towards AI, and there are differences in willingness to use based on factors such as gender and level of attention.

  Key words: Artificial Intelligence, Physician, Attitude, Willingness

  Submit time: 12 December 2023

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
• html

• 图表

• Ming Li. Analysis of doctors' willingness to use medical artificial intelligence and influencing factors. 2023. doi: 10.12201/bmr.202307.00001

  dongtingting. Study on the safety impact and countermeasures of medical artificial intelligence products on patients. 2020. doi: 10.12201/bmr.202012.00004

  xiejunxiang. The composition of Key Technologies and Application in Medical Artificial Intelligence. 2020. doi: 10.12201/bmr.202005.00243

  Li Ping, Sun Liping, Ren He, Li Huaping, Shao Zeguo. Construction of artificial intelligence thinking training course system for medical device specialty. 2020. doi: 10.12201/bmr.202007.00013

  wusijing, xubinbin, huangfeng. Development Status and Policy Suggestions of Medical Artificial Intelligence in Zhejiang Province. 2021. doi: 10.12201/bmr.202101.00015

  ZhengYanli, Han Fuhai, LI Shuyu, SU Wenxing. Application Status and Prospect of Artificial Intelligence Large Models in Medicine. 2023. doi: 10.12201/bmr.202312.00027

  wangchen, WangXiaofan. Research on the Willingness of Medical E-commerce and Its Influencing Factors. 2020. doi: 10.12201/bmr.202008.00014

  Dong Kun, Yang Fen, Yang Yang. Application and Thinking of Artificial Intelligence in General Practitioner Training. 2023. doi: 10.12201/bmr.202305.00011

  Yufan Zhu, Xin Zhao, Zhiqiang Yang, Houcheng Zhong, Lin Cai, Yuanlong Xie. Prospect of the Training Model for Artificial Intelligence + Medicine Inter-disciplinary Talents. 2020. doi: 10.12201/bmr.202008.00010

  yanwencheng, lijunzhen, chenmei, 卢艳红. Artificial Intelligence for Stomatology: Application in Dental Endodontics. 2021. doi: 10.12201/bmr.202102.00003

• ID	Submit time	Number	Download
  1	2023-07-04	bmr.202312.00020V1	Download

• Public  Anonymous  To author only Submit Comments