Liability for harm caused by AI in healthcare: an overview of the core legal concepts.
Liability for harm caused by AI in healthcare: an overview of the core legal concepts.
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DOI：
10.3389/fphar.2023.1297353
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发表时间：
2023
影响因子：
5.6
通讯作者：
中科院分区：
文献类型：
--
作者：
人工智能(AI)与非洲医疗保健的融合带来了变革性的机遇，但也带来了深刻的法律挑战，特别是在责任方面。随着人工智能变得更加自主，当事情出错时，确定谁或什么应该负责变得模糊起来。本文旨在回顾与医疗保健中人工智能造成的损害责任问题相关的法律概念。尽管一些人建议将法律人格归因于人工智能作为一种潜在的解决方案，但这种做法的可行性仍存在争议。委托代理关系，即医生对人工智能决策负责，由于潜在的责任，有可能减少人工智能工具的采用。同样，使用产品法来确定责任也是有问题的，因为人工智能的动态学习性质与静态产品背道而驰。这种流动性使产品缺陷的传统定义复杂化，进而使责任所在之处复杂化。在探索替代方案时，基于风险的责任确定将重点放在潜在的危险上，而不是具体的过错分配上，成为一条潜在的途径。然而，这些也给问责制度的分配带来了挑战。严格责任已被提议作为另一种途径。它可以通过关注损害而不是过错来简化对受害者的赔偿程序。然而，对利益相关者的经济影响、不公正的声誉损害的可能性以及全球应用的可行性引发了担忧。与基于责任的方法不同，和解在促进监管沙盒方面有着很大的希望。总而言之，虽然人工智能系统融入医疗保健具有巨大的潜力，但它需要重新评估我们的法律框架。核心挑战是如何使传统的责任概念适应人工智能的新奇和不可预测的性质--或者从责任转向和解。未来的讨论和研究必须驾驭这些复杂的水域，并寻求既确保进步又确保保护的解决方案。
The integration of artificial intelligence (AI) into healthcare in Africa presents transformative opportunities but also raises profound legal challenges, especially concerning liability. As AI becomes more autonomous, determining who or what is responsible when things go wrong becomes ambiguous. This article aims to review the legal concepts relevant to the issue of liability for harm caused by AI in healthcare. While some suggest attributing legal personhood to AI as a potential solution, the feasibility of this remains controversial. The principal–agent relationship, where the physician is held responsible for AI decisions, risks reducing the adoption of AI tools due to potential liabilities. Similarly, using product law to establish liability is problematic because of the dynamic learning nature of AI, which deviates from static products. This fluidity complicates traditional definitions of product defects and, by extension, where responsibility lies. Exploring alternatives, risk-based determinations of liability, which focus on potential hazards rather than on specific fault assignments, emerges as a potential pathway. However, these, too, present challenges in assigning accountability. Strict liability has been proposed as another avenue. It can simplify the compensation process for victims by focusing on the harm rather than on the fault. Yet, concerns arise over the economic impact on stakeholders, the potential for unjust reputational damage, and the feasibility of a global application. Instead of approaches based on liability, reconciliation holds much promise to facilitate regulatory sandboxes. In conclusion, while the integration of AI systems into healthcare holds vast potential, it necessitates a re-evaluation of our legal frameworks. The central challenge is how to adapt traditional concepts of liability to the novel and unpredictable nature of AI—or to move away from liability towards reconciliation. Future discussions and research must navigate these complex waters and seek solutions that ensure both progress and protection.
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影响因子：
0.9
作者：
Naidoo, S;Bottomley, D;Naidoo, M;Donnelly, D;Thaldar, D W
通讯作者：
Thaldar, D W
DOI：
10.1007/s10728-021-00430-4
发表时间：
2021-09
期刊：
Health care analysis : HCA : journal of health philosophy and policy
影响因子：
--
作者：
Holm S;Stanton C;Bartlett B
通讯作者：
Bartlett B
影响因子：
--
作者：
Owoyemi A;Owoyemi J;Osiyemi A;Boyd A
通讯作者：
Boyd A
影响因子：
7.4
作者：
Floridi, Luciano;Cowls, Josh;Vayena, Effy
通讯作者：
Vayena, Effy
影响因子：
2.9
作者：
Bertolini, Andrea;Episcopo, Francesca
通讯作者：
Episcopo, Francesca