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Machine Learning for Diagnosis and Treatment:

Gymnastics for the GDPR

DOI https://doi.org/10.21552/edpl/2018/3/11

Robin Pierce


Machine Learning (ML), a form of artificial intelligence (AI) that produces iterative refinement of outputs without human intervention, is gaining traction in healthcare as a promising way of streamlining diagnosis and treatment and is even being explored as a more efficient alternative to clinical trials. ML is increasingly being identified as an essential tool in the arsenal of Big Data for medicine. ML can process and analyse the data resulting in outputs that can inform treatment and diagnosis. Consequently, ML is likely to occupy a central role in precision medicine, an approach that tailors treatment based on characteristics of individual patients instead of traditional ‘average’ or one-size-fits-all medicine, potentially optimising outcomes as well as resource allocation. ML falls into a category of data-reliant technologies that have the potential to enhance healthcare in significant ways. However, as such, concerns about data protection and the GDPR may arise as ML assumes a growing role in healthcare, prompting questions about the extent to which the GDPR and related legislation will be able to provide adequate data protection for data subjects. Focusing on issues of transparency, fairness, storage limitation, purpose limitation and data minimisation as well as specific provisions supporting these principles, this article examines the interaction between ML and data protection law.
Keywords: Machine Learning, GDPR, Data Protection, Artificial Intelligence in Medicine, Health Data, Automated Processing, Data Minimisation

Robin Pierce, JD, PhD, Tilburg Institute for Law, Technology and Society, Tilburg University, The Netherlands. For correspondence: <mailto:r.l.pierce@tilburguniversity.edu>.

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