检验医学中的人工智能:基本伦理问题与规范要点

F. Pennestrì, G. Banfi
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引用次数: 12

摘要

检验医学在提供基于价值的护理方面的贡献取决于病理学家和临床医生之间的积极合作和信任。一般来说,药物的有效性更多地取决于临床医生和病人之间的积极合作和信任。从20世纪下半叶开始,医学艺术受到人工智能(AI)技术传播的挑战,最近在一些诊断专业上显示出与有血有肉的医生相当的表现。作为医学数据的主要来源,实验室是人工智能技术发挥最大潜力的天然场所。为了最大限度地提高预期结果并最大限度地降低风险,必须通过设计定义数据收集和解释的道德要求,明确人工智能技术的特定使用是否会增强或挑战这些要求,并在严格但可行的规范下保存这些数据。从2018年开始,欧盟委员会(EC)正在努力从文化和规范的角度为欧洲国家和合作伙伴之间的可持续人工智能发展奠定基础。除了欧共体的工作外,联合王国还提供了值得考虑的补充建议,以便将科学技术用于为患者和医生服务。在本文中,我们讨论了与在病理学和实验室医学中使用人工智能技术相关的主要伦理挑战,并总结了前面提到的指导方针和框架中最相关的关键点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence in laboratory medicine: fundamental ethical issues and normative key-points
Abstract The contribution of laboratory medicine in delivering value-based care depends on active cooperation and trust between pathologist and clinician. The effectiveness of medicine more in general depends in turn on active cooperation and trust between clinician and patient. From the second half of the 20th century, the art of medicine is challenged by the spread of artificial intelligence (AI) technologies, recently showing comparable performances to flesh-and-bone doctors in some diagnostic specialties. Being the principle source of data in medicine, the laboratory is a natural ground where AI technologies can disclose the best of their potential. In order to maximize the expected outcomes and minimize risks, it is crucial to define ethical requirements for data collection and interpretation by-design, clarify whether they are enhanced or challenged by specific uses of AI technologies, and preserve these data under rigorous but feasible norms. From 2018 onwards, the European Commission (EC) is making efforts to lay the foundations of sustainable AI development among European countries and partners, both from a cultural and a normative perspective. Alongside with the work of the EC, the United Kingdom provided worthy-considering complementary advice in order to put science and technology at the service of patients and doctors. In this paper we discuss the main ethical challenges associated with the use of AI technologies in pathology and laboratory medicine, and summarize the most pertaining key-points from the guidelines and frameworks before-mentioned.
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