litao, fenghexia. Innovative Applications, Risk Challenges, and Governance Countermeasures of Artificial Intelligence in the Healthcare Industry. 2025. biomedRxiv.202501.00067
Innovative Applications, Risk Challenges, and Governance Countermeasures of Artificial Intelligence in the Healthcare Industry
Corresponding author: fenghexia, fenghexia2008@163.com
DOI: 10.12201/bmr.202501.00067
-
Abstract: This study provides a comprehensive examination of the innovative applications of artificial intelligence (AI) within the healthcare sector, encompassing areas such as health services, medical diagnostics, health management, and pharmaceutical development,. It further explores the multifaceted risk challenges posed by AI in healthcare, including issues related to data privacy and security, algorithmic bias, legal and regulatory constraints, technical reliability, and ethical dilemmas. To address these identified challenges, this paper proposes a series of governance countermeasures, which include establishing robust data protection frameworks, enhancing the technical reliability and fairness of AI systems, developing a globally coordinated multi-level regulatory structure, formulating standardized data protocols, unlocking the latent potential of healthcare data, and fostering a harmonious balance between technological innovation and humanistic care. These measures aim to maximize the potential benefits of AI technologies, realize the inherent value of healthcare data, and promote the sustainable, high-quality development of the healthcare industry.
Key words: artificial; intelligence, healthcare, digital; health, governance; countermeasuresSubmit time: 22 January 2025
Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity. -
图表
-
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
GE Xiaoling. Application of Artificial Intelligence Large Models in Healthcare:a Survey. 2024. doi: 10.12201/bmr.202408.00039
rui chen, chen yueqi, li jinbin, zhang shengfa. Progress and Trend of the Application of Artificial Intelligence in the Basic Health Management of Type 2 Diabetes. 2025. doi: 10.12201/bmr.202506.00072
Wang Qian, Liu Xiangmin, Chen Lingyun, Fang An. Roadmap of Data Governance to Artificial Intelligence in National Institutes of Health. 2025. doi: 10.12201/bmr.202505.00026
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
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
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
Dong Kun, Yang Fen, Yang Yang. Application and Thinking of Artificial Intelligence in General Practitioner Training. 2023. doi: 10.12201/bmr.202305.00011
wanglong. Application of Artificial Intelligence in Acupuncture Point Location and Prescription Optimization. 2025. doi: 10.12201/bmr.202508.00053
tianrui. Research on Countermeasures of Participating in False Information in Health Governance by University Medical Libraries. 2023. doi: 10.12201/bmr.202303.00005
-
ID Submit time Number Download 1 2025-01-03 10.12201/bmr.202501.00067V1
Download -
-
Public Anonymous To author only
Get Citation
Article Metrics
- Read: 883
- Download: 0
- Comment: 0

Login
Register




京公网安备