Suppr超能文献

破解密码:一项范围综述,旨在联合多学科解决健康人工智能中的法律问题。

Cracking the code: a scoping review to unite disciplines in tackling legal issues in health artificial intelligence.

作者信息

Nunnelley Sophie, Flood Colleen M, Da Silva Michael, Horsley Tanya, Kanathasan Sarathy, Thomas Bryan, Da Silva Emily Ann, Ly Valentina, Daniel Ryan C, Sheikh Hassani Mohsen, Singh Devin

机构信息

Lincoln Alexander School of Law, Toronto Metropolitan University, Toronto, Ontario, Canada

Faculty of Law, Queen's University, Kingston, Ontario, Canada.

出版信息

BMJ Health Care Inform. 2025 Apr 10;32(1):e101112. doi: 10.1136/bmjhci-2024-101112.

Abstract

OBJECTIVES

The rapid integration of artificial intelligence (AI) in healthcare requires robust legal safeguards to ensure safety, privacy and non-discrimination, crucial for maintaining trust. Yet, unaddressed differences in disciplinary perspectives and priorities risk impeding effective reform. This study uncovers convergences and divergences in disciplinary comprehension, prioritisation and proposed solutions to legal issues with health-AI, providing law and policymaking guidance.

METHODS

Employing a scoping review methodology, we searched MEDLINE (Ovid), EMBASE (Ovid), HeinOnline Law Journal Library, Index to Foreign Legal Periodicals (HeinOnline), Index to Legal Periodicals and Books (EBSCOhost), Web of Science (Core Collection), Scopus and IEEE Xplore, identifying legal issue discussions published, in English or French, from January 2012 to July 2021. Of 18 168 screened studies, 432 were included for data extraction and analysis. We mapped the legal concerns and solutions discussed by authors in medicine, law, nursing, pharmacy, other healthcare professions, public health, computer science and engineering, revealing where they agree and disagree in their understanding, prioritisation and response to legal concerns.

RESULTS

Critical disciplinary differences were evident in both the frequency and nature of discussions of legal issues and potential solutions. Notably, innovators in computer science and engineering exhibited minimal engagement with legal issues. Authors in law and medicine frequently contributed but prioritised different legal issues and proposed different solutions.

DISCUSSION AND CONCLUSION

Differing perspectives regarding law reform priorities and solutions jeopardise the progress of health AI development. We need inclusive, interdisciplinary dialogues concerning the risks and trade-offs associated with various solutions to ensure optimal law and policy reform.

摘要

目标

人工智能(AI)在医疗保健领域的迅速整合需要强有力的法律保障措施,以确保安全、隐私和非歧视,这对于维持信任至关重要。然而,学科观点和优先事项上未得到解决的差异可能会阻碍有效的改革。本研究揭示了在对健康人工智能法律问题的学科理解、优先排序及提出的解决方案方面的异同,为法律制定和政策制定提供指导。

方法

采用范围综述方法,我们检索了MEDLINE(Ovid)、EMBASE(Ovid)、HeinOnline法律期刊图书馆、《外国法律期刊索引》(HeinOnline)、《法律期刊与书籍索引》(EBSCOhost)、科学引文索引(核心合集)、Scopus和IEEE Xplore,识别2012年1月至2021年7月以英文或法文发表的法律问题讨论。在筛选的18168项研究中,432项被纳入数据提取和分析。我们梳理了医学、法律、护理、药学、其他医疗保健专业、公共卫生、计算机科学和工程领域的作者所讨论的法律问题和解决方案,揭示了他们在对法律问题的理解、优先排序及应对方面的异同。

结果

在法律问题和潜在解决方案的讨论频率和性质方面,明显存在关键的学科差异。值得注意的是,计算机科学和工程领域的创新者对法律问题的参与度极低。法律和医学领域的作者经常发表意见,但他们优先考虑的法律问题不同,提出的解决方案也不同。

讨论与结论

关于法律改革优先事项和解决方案的不同观点危及健康人工智能发展的进程。我们需要就各种解决方案相关的风险和权衡进行包容性的跨学科对话,以确保实现最佳的法律和政策改革。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f823/11987151/0c4836d9d46e/bmjhci-32-1-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验