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Machine learning in the EU health care context: exploring the ethical, legal and social issues

机译：欧盟医疗保健背景下的机器学习：探索道德，法律和社会问题

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摘要

Diagnosis and clinical decision-making based on Machine Learning technologies are showing significant advances that may change the functioning of our health care systems. They promise more effective and efficient healthcare at a lower cost. Even though evidence suggests that all these promises have yet to be demonstrated in clinical practice, it is undeniable that these technologies are already re-signifying the relationships on the health care landscape, particularly in the physician-patient relationship, which we can already redefine as a 'physician-computer-patient relationship'. This new scenario is undoubtedly promising, but it also poses some fundamental issues that need an urgent answer. An inappropriate use of Machine Learning might involve a dramatic loss in the patients' rights to informed consent or possible discrimination reflecting their personal circumstances. Unfortunately, the traditional principles incorporated by medical law are insufficient to face this challenge. Our most recent regulatory framework, defined by the General Regulation on Data Protection, might be useful in order to avoid this scenario since it includes the right not to be subject to a decision based solely on automated processing. In this paper, however, we argue that this legal tool is adequate but not sufficient to address the legal, ethical and social challenges that Machine Learning technologies pose to patients' rights and health care givers' capacities. Therefore, further development of the regulation on this topic and the development of new actors such as the Health Information Counsellors, will be necessary.
机译：基于机器学习技术的诊断和临床决策显示出可能改变医疗保健系统的运作的重要进步。他们承诺以较低的成本保证更有效和高效的医疗保健。尽管证据表明，所有这些承诺尚未在临床实践中表现出来，但不可否认的是，这些技术已经重新阐述了医疗保健景观的关系，特别是在医生 - 患者关系中，我们已经可以重新定义'医师 - 计算机患者关系'。这种新情景无疑有前途，但它也造成了一些需要紧急答案的基本问题。不恰当的使用机器学习可能涉及患者在患者的知情同意或反映其个人情况的歧视的权利中急剧损失。不幸的是，医学法纳入的传统原则不足以面临这一挑战。我们最近的监管框架，由数据保护的一般规定定义，可能是有用的，以避免这种情况，因为它包括不仅根据自动处理的决定而非决定的权利。然而，在本文中，我们争辩说，这种法律工具足以满足机器学习技术对患者权利和医疗保健赋权者能力的法律，道德和社会挑战。因此，将进一步制定对该主题的规定和新行为者的发展，如健康信息辅导员，如健康信息顾问。

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  • 来源
    《Information Communication & Society》 |2020年第8期|1139-1153|共15页
  • 作者

    de Miguel Inigo; Sanz Begona; Lazcoz Guillermo;

  • 作者单位

    Univ Basque Country Dept Publ Law UPV EHU Chair Law Leioa Spain|Univ Basque Country Dept Publ Law UPV EHU Human Genome Res Grp Leioa Spain|Basque Fdn Sci Ikerbasque Bilbao Spain;

    Univ Basque Country Fac Med & Nursery Dept Physiol UPV EHU Bilbao Spain|BioCruces Hlth Res Inst Baracaldo Spain;

    Univ Basque Country Dept Publ Law UPV EHU Chair Law Leioa Spain|Univ Basque Country Dept Publ Law UPV EHU Human Genome Res Grp Leioa Spain;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Machine learning; black box; medicine; GDPR; healthcare;

    机译：机器学习;黑匣子;医学;GDPR;医疗保健;

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