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

ABSTRACT

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.

KEYWORDS:

• Machine learning
• black box
• medicine
• GDPR
• healthcare

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Iñigo de Miguel is a distinguished researcher at the University of the Basque Country. He has published more than a hundred articles and book chapters on topics related to bioethics.

Begoña Sanz is a Member of the Ageing On Research Team/Group of the University of the Basque Country. She holds a Ph.D. on biochemistry and molecular biology. Her research is focused on the study of molecular biomarkers related to ageing linked pathologies.

Guillermo Lazcoz is a Ph.D. student at the University of the Basque Country. His work is related to AI ethical and legal issues.

Additional information

Funding

This work was supported by Eusko Jaurlaritza [grant number Ayudas a grupos de investigación IT-1066-16]; H2020 Science with and for Society [grant number GRANT AGREEMENT NUMBER — 788039 — PANELFIT].