Privacy Protection in Using Artificial Intelligence for Healthcare: Chinese Regulation in Comparative Perspective
Open accessAbstract
Advanced artificial intelligence (AI) technologies are now widely employed in China’s medical and healthcare fields. Enormous amounts of personal data are collected from various sources and inserted into AI algorithms for medical purposes, producing challenges to patient’s privacy. This is a comparative study of Chinese, United States, and European Union operational rules for healthcare data that is collected and then used in AI functions, particularly focusing on legal differences and deficiencies. The conceptual boundaries of privacy and personal information, the influence of technological development on the informed consent model, and conflicts between freedom and security in rules of cross-border data flow were found to be key issues requiring consideration when regulating healthcare data used for AI purposes. Furthermore, the results indicate that the appropriate balance between privacy protections and technological development, between individual and group interests, and between corporate profits and the public interest should be identified and observed. In terms of specific rule-making, it was found that China should establish special regulations protecting healthcare information, provide clear definitions and classification schemas for different types of healthcare information, and enact stricter accountability mechanisms. Examining and contrasting operational rules for AI in health care promotes informed privacy governance and improved privacy legislation.
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The risk of privacy infringement due to misuse of pseudo-anonymized patient data is a primary concern.
“The risk of privacy infringement due to the misuse of pseudo-anonymized patient data [7,8] is a primary concern.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [7]
Sophisticated techniques can potentially re-engineer anonymized data to identify individuals, thus breaching patient confidentiality.
“Sophisticated techniques can potentially re-engineer anonymized data to identify individuals, thus breaching patient confidentiality.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [7]
Blockchain can be integrated with AI and IoT devices to enable secure and real-time monitoring of patient health data, providing healthcare professionals with reliable data for decision-making while preserving patient privacy.
“Moreover, blockchain can be integrated with other technologies, such as AI and IoT devices, to enable secure and real-time monitoring of patient health data, providing healthcare professionals with reliable data for decision-making while preserving patient privacy [7,12].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [7]
Blockchain technology provides a secure, decentralized method for storing and managing healthcare data, using a distributed ledger system to ensure patient records are immutable and verifiable, thereby thwarting unauthorized access and potential data breaches.
“Blockchain technology provides a secure, decentralized method for storing and managing healthcare data, utilizing a distributed ledger system to ensure that patient records are immutable and verifiable, thereby thwarting unauthorized access and potential data breaches.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [7]
Mishandling sensitive medical data could result in significant privacy violations and misuse.
“Mishandling such data could result in significant privacy violations and misuse [7,12].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [7]
Data integrity is essential for ensuring reliable healthcare outcomes.
“Additionally, the authors of [7,8] emphasize the essential role of data integrity in ensuring reliable healthcare outcomes.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [7]
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- Authors
- Wang, C., Zhang, J., Lassi, N., & Zhang, X.
- Affiliations
- Zhejiang University (CN)
- Corresponding author
- Zhang, Xiaohan
- Venue
- Healthcare
- Publisher
- MDPI AG
- Published
- 2022-09-27
- Volume
- 10
- Issue
- 10
- Pages
- 1878
- ISSN
- 2227-9032 (ISSN-L)
- PMC
- PMC9601726
- OpenAlex
- W4297539945
- Cited by
- 115
- References
- 15
- Citations by year
- 2026: 24 · 2025: 43 · 2024: 30 · 2023: 18
- FWCI
- 140.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Healthcare (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fundamental Research Funds for the Central Universities (22LLXC02Z)
- Cross-Disciplinary Research Project of Zhejiang University (22LLXC02Z)
- Digital Silk Road Research Project of Zhejiang Province (22LLXC02Z)
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Information Systems › Thoreau and American Literature
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence in healthcare, patient privacy, personal data protection, informed consent, cross-border data flows, China, United States, European Union, comparative privacy law, accountability mechanisms
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