Christina Etteldorf

European Data Protection Law Review, Volume 6 (2020), Issue 2, Page 265 - 280

Anna Berti Suman, Robin Pierce

European Data Protection Law Review, Volume 4 (2018), Issue 3, Page 284 - 295

Present discussions on the implications of the GDPR for medical practice and health research mostly target the passive collection of health data. This article shifts the lens of analysis to the scarcely researched and rather different phenomenon of the active sharing of health data within the framework of Citizen Science projects. Starting from this focus, the article queries whether data processing requirements under the GDPR impacts the advancement of Citizen Science for health research. A number of tensions between the two aims are identified both in abstract terms and ‘in practice’ by analysing three Citizen Science scenarios and drawing parallels with the experience of ‘collective’ Clinical Trials. The limited literature on the topic makes this article an exploratory reflection on key tensions, with the aim of opening the way for further research. This discussion is inspired by the need to guarantee that opportunities of Citizen Science will not be unduly curtailed by the advent of the GDPR but also to ensure that Citizen Science is implemented in ways that are consistent with the GDPR. Keywords: Citizen Science, Open Science, GDPR, Secondary Use of Health Data, Research Exemption

Robin Pierce

European Data Protection Law Review, Volume 4 (2018), Issue 3, Page 333 - 343

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

Gauthier Chassang, Tom Southerington, Olga Tzortzatou, Martin Boeckhout, Santa Slokenberga

European Data Protection Law Review, Volume 4 (2018), Issue 3, Page 296 - 307

This article examines the content of data portability right (II), operationalisation of data portability in health research context and related challenges (III) by considering both GDPR provisions and special Guidelines from the European Data Protection Board (ex-Article 29 Data Protection Working Party). We provide in depth analysis of the provisions and tables for easing the identification of potential implementation of data portability in health research contexts. Keywords: Data Portability, Scientific Research, GDPR, Health Data, Data Subject's Rights, European Union