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

Corresponding author: Ming Li, doctorliming@foxmail.com

DOI: 10.12201/bmr.202307.00001

•  

  Abstract: 【Abstract】OBJECTIVE: To explore the perceptions, willingness, and influencing factors of Chinese physicians towards medical AI using the UTAUT model. METHODS: A 30-question closed-ended questionnaire was distributed via WeChat, and the results were analyzed deively, and with relevant correlation variables, to analyze the influencing factors related to trust, and willingness.RESULTS: In terms of trust in AI, 4 (1.22%) were very trusting, 83 (25.38%) were relatively trusting, 219 (66.97%) were average, 19 (5.81%) were relatively distrustful, and 2 (0.61%) were very distrustful in terms of willingness, In terms of willingness to use, of all the participants, 170 (51.99%) actively used it, 81 (24.77%) chose to follow it, 44 (13.46%) used it when instructed by leaders, 16 people (4.89%) were interested in it, 16 people (4.89%) had no interest in it. At the same time, There were significant differences in the group of gender (P=0.017), educational background (P=0.045), doctors' attention to AI (P=0.000), whether they were exposed to medical AI (P=0.000), and the number of mobile apps (P=0.000).CONCLUSION: Most human physicians showed positive attitudes toward AI, and there were differences in willingness to use sex, attention, and other factors.

  Key words: Artificial intelligence; medicine; doctors; attitudes; willingness

  Submit time: 12 September 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

• Li Ming. Analysis of doctors willingness to use medical artificial intelligence and influencing factors. 2023. doi: 10.12201/bmr.202312.00020

  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

  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

  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

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

  lvshuzhen. Study on the Factors Influencing Patients Willingness to Use Chinese Medicine Delivery Service Based on Hospital WeChat Public Platform. 2022. doi: 10.12201/bmr.202209.00010

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

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

• ID	Submit time	Number	Download
  1	2023-07-04	bmr.202307.00001V1	Download

• Public  Anonymous  To author only Submit Comments