{"title":"[Legal Risk Assessment and Prevention in Artificial Intelligence-Assisted Health Care].","authors":"Jinming Yang, Na Wang, Yexun Hu, Wei Zhang","doi":"10.12182/20250160301","DOIUrl":null,"url":null,"abstract":"<p><p>With the wide application of new technologies such as large language models and generative artificial intelligence (AI) in the health care sector, artificial intelligence-assisted health care is confronted with new forms of legal risks. The algorithmic bias and data security issues in AI-assisted health care have given rise to risks of infringement on general personality rights and specific personality rights. The handling of health care data and the distribution of profits from health care data have spawned disputes over data property rights. Moreover, there will also be risks of uncertainties in the attribution of liability for medical harms once AI technology becomes deeply embedded in health care. Based on the emerging changes in the legal risks associated with AI-assisted health care, it is necessary to establish a corresponding algorithm review mechanism to eliminate algorithm biases, improve the data management system through a whole-life cycle approach to ensure data security, define hierarchical data property rights and establish authorization rules to resolve property rights disputes, and reasonably assign tort liability for medical harms based on specific faults.</p>","PeriodicalId":39321,"journal":{"name":"四川大学学报(医学版)","volume":"56 1","pages":"143-148"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914023/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"四川大学学报(医学版)","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.12182/20250160301","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
With the wide application of new technologies such as large language models and generative artificial intelligence (AI) in the health care sector, artificial intelligence-assisted health care is confronted with new forms of legal risks. The algorithmic bias and data security issues in AI-assisted health care have given rise to risks of infringement on general personality rights and specific personality rights. The handling of health care data and the distribution of profits from health care data have spawned disputes over data property rights. Moreover, there will also be risks of uncertainties in the attribution of liability for medical harms once AI technology becomes deeply embedded in health care. Based on the emerging changes in the legal risks associated with AI-assisted health care, it is necessary to establish a corresponding algorithm review mechanism to eliminate algorithm biases, improve the data management system through a whole-life cycle approach to ensure data security, define hierarchical data property rights and establish authorization rules to resolve property rights disputes, and reasonably assign tort liability for medical harms based on specific faults.
四川大学学报(医学版)Biochemistry, Genetics and Molecular Biology-Molecular Biology
CiteScore
0.70
自引率
0.00%
发文量
8695
期刊介绍:
"Journal of Sichuan University (Medical Edition)" is a comprehensive medical academic journal sponsored by Sichuan University, a higher education institution directly under the Ministry of Education of the People's Republic of China. It was founded in 1959 and was originally named "Journal of Sichuan Medical College". In 1986, it was renamed "Journal of West China University of Medical Sciences". In 2003, it was renamed "Journal of Sichuan University (Medical Edition)" (bimonthly).
"Journal of Sichuan University (Medical Edition)" is a Chinese core journal and a Chinese authoritative academic journal (RCCSE). It is included in the retrieval systems such as China Science and Technology Papers and Citation Database (CSTPCD), China Science Citation Database (CSCD) (core version), Peking University Library's "Overview of Chinese Core Journals", the U.S. "Index Medica" (IM/Medline), the U.S. "PubMed Central" (PMC), the U.S. "Biological Abstracts" (BA), the U.S. "Chemical Abstracts" (CA), the U.S. EBSCO, the Netherlands "Abstracts and Citation Database" (Scopus), the Japan Science and Technology Agency Database (JST), the Russian "Abstract Magazine", the Chinese Biomedical Literature CD-ROM Database (CBMdisc), the Chinese Biomedical Periodical Literature Database (CMCC), the China Academic Journal Network Full-text Database (CNKI), the Chinese Academic Journal (CD-ROM Edition), and the Wanfang Data-Digital Journal Group.