Vandemeulebroucke Tijs
Bonn Sustainable AI Lab, Institut für Wissenschaft und Ethik, Universität Bonn-University of Bonn, Bonner Talweg 57, 53113, Bonn, Germany.
Pflugers Arch. 2025 Apr;477(4):591-601. doi: 10.1007/s00424-024-02984-3. Epub 2024 Jul 6.
Artificial intelligence systems (ai-systems) (e.g. machine learning, generative artificial intelligence), in healthcare and medicine, have been received with hopes of better care quality, more efficiency, lower care costs, etc. Simultaneously, these systems have been met with reservations regarding their impacts on stakeholders' privacy, on changing power dynamics, on systemic biases, etc. Fortunately, healthcare and medicine have been guided by a multitude of ethical principles, frameworks, or approaches, which also guide the use of ai-systems in healthcare and medicine, in one form or another. Nevertheless, in this article, I argue that most of these approaches are inspired by a local isolationist view on ai-systems, here exemplified by the principlist approach. Despite positive contributions to laying out the ethical landscape of ai-systems in healthcare and medicine, such ethics approaches are too focused on a specific local healthcare and medical setting, be it a particular care relationship, a particular care organisation, or a particular society or region. By doing so, they lose sight of the global impacts ai-systems have, especially environmental impacts and related social impacts, such as increased health risks. To meet this gap, this article presents a global approach to the ethics of ai-systems in healthcare and medicine which consists of five levels of ethical impacts and analysis: individual-relational, organisational, societal, global, and historical. As such, this global approach incorporates the local isolationist view by integrating it in a wider landscape of ethical consideration so to ensure ai-systems meet the needs of everyone everywhere.
在医疗保健和医学领域，人工智能系统（如机器学习、生成式人工智能）被寄予了提升医疗质量、提高效率、降低医疗成本等厚望。与此同时，这些系统在对利益相关者隐私的影响、权力动态变化、系统性偏见等方面也引发了一些疑虑。幸运的是，医疗保健和医学领域一直遵循着众多伦理原则、框架或方法，这些原则、框架或方法也以某种形式指导着人工智能系统在医疗保健和医学中的应用。然而，在本文中，我认为这些方法大多受到对人工智能系统的局部孤立主义观点的启发，这里以原则主义方法为例。尽管这些伦理方法在勾勒医疗保健和医学中人工智能系统的伦理图景方面做出了积极贡献，但它们过于关注特定的局部医疗保健和医学环境，无论是特定的医患关系、特定的医疗机构，还是特定的社会或地区。这样一来，它们忽视了人工智能系统产生的全球影响，尤其是环境影响和相关的社会影响，如健康风险增加。为了弥补这一差距，本文提出了一种针对医疗保健和医学中人工智能系统伦理的全球方法，该方法由五个伦理影响和分析层面组成：个人关系层面、组织层面、社会层面、全球层面和历史层面。因此，这种全球方法通过将局部孤立主义观点纳入更广泛的伦理考量范围，从而确保人工智能系统满足世界各地每个人的需求。
