A Study of Ethical Risks of Medical Artificial Intelligence Applications from the Perspective of the Public in Society

Corresponding author: Wangruojia, ruojia_wang@bucm.edu.cn

DOI: 10.12201/bmr.202511.00062

•  

  Abstract: Purpose/Significance To deeply explore the ethical risks of applying artificial intelligence in the medical field from the perspective of the general public, and on this basis, propose countermeasures.Method/Process Focusing on the two mainstream social platforms, Bilibili and Douyin, collecting text data of comments related to medical artificial intelligence posted by users. Based on the computational grounded theory method, the collected comment data is analyzed through three steps: pattern recognition, pattern improvement, and pattern confirmation. Result/Conclusion Through the analysis of user comments, four types of ethical risks were ultimately classified, namely legal ethical risk, humanistic ethical risk, algorithmic ethical risk and data ethical risk. This revealed the publics multi-dimensional concerns about the application risks of artificial intelligence in the medical field and proposed corresponding countermeasures from different dimensions.

  Key words: medical artificial intelligenc; ethical risk; computational grounded theory

  Submit time: 21 November 2025

  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

• dongyi, Ran ye, Yu zhong guang. Research on the current status of medical artificial intelligence application risk research and its identification in China.Dong yi1,Ran ye1,Yu zhong guang2,3.. 2024. doi: 10.12201/bmr.202411.00081

  Li Yuhang, Dai hui. Clinical Ethical Governance of Generative Medical Artificial Intelligence (GMAI): A Three-Dimensional Collaborative Pathway and Chinese Practices. 2025. doi: 10.12201/bmr.202511.00057

  litao, fenghexia. Innovative Applications, Risk Challenges, and Governance Countermeasures of Artificial Intelligence in the Healthcare Industry. 2025. doi: 10.12201/bmr.202501.00067

  shisenzhong. Analysis of the Risks and Governance Strategies for the Application of Generative Artificial Intelligence (GAI) in Primary Healthcare. 2024. doi: 10.12201/bmr.202408.00053

  GUO Yifan, GUO Senyu. From Relational Embeddedness to Structural Embeddedness: Mechanisms and Pathways of Artificial Intelligence Integration into the Healthcare Service System: A Grounded Theory-Based Policy Text Analysis. 2025. doi: 10.12201/bmr.202508.00039

  GE Xiaoling. Application of Artificial Intelligence Large Models in Healthcare:a Survey. 2024. doi: 10.12201/bmr.202408.00039

  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

  zhangshihong, hu hongpu. Application Scenarios and Challenges of Artificial Intelligence in Public Health Emergency Management. 2025. doi: 10.12201/bmr.202511.00063

  wang lei, liu miao, wang qian, fang an. Data Risks, Compliance Obligations, and Countermeasures for Medical Corpora in Generative AI. 2025. doi: 10.12201/bmr.202512.00083

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

• ID	Submit time	Number	Download
  1	2025-10-02	10.12201/bmr.202511.00062V1	Download

• Public  Anonymous  To author only Submit Comments