AI Technologies and Accountability in Digital Health

Forthcoming 2021, Cambridge Bioethics and the Law Series, Cambridge University Press.

30 Pages Posted: 19 Apr 2021 Last revised: 19 May 2021

Date Written: April 30, 2021


How to build an ecosystem of trust in digital health? The availability of large amounts of personal data, from multimodal sources, combined with AI and ML capacities, Internet of Things and strong computational platforms have the potential to transform healthcare systems in a disruptive way. The emergence of personalized medicine offers opportunities and raises new legal, ethical and societal challenges. A silent transformation towards a data-driven preventive and personalized medicine may improve diagnosis and therapies while reducing the cost of public health policy. In order to build an ecosystem of trust, the risks of harm and misuses such as data breaches, privacy issues, discrimination, eugenics must be addressed. This chapter presents the disruptive nature of AI and ML technologies in healthcare, and makes specific recommendations to build a trustworthy digital health system. Special attention is given to the governance of AI systems by international institutions (EU Proposal on AI Regulation, OECD, Council of Europe, etc) but also on key principles like transparency, accountability and decision-making processes in a medical context. We will first identify the key parameters to advance the field of digital health in a responsible way. Secondly, we propose possible solutions to shape a sound policy in digital health taking into account a rights-based governance framework. The last part of the chapter will be dedicated to the accountability scheme.

Keywords: Trustworthy Artificial Intelligence, Digital Health, Personalized Medicine, Human Rights, Data Protection, Digital Ethics, Data Governance, Internet of Things, Blockchain

Suggested Citation

Thelisson, Eva, AI Technologies and Accountability in Digital Health (April 30, 2021). Forthcoming 2021, Cambridge Bioethics and the Law Series, Cambridge University Press. , Available at SSRN:

Eva Thelisson (Contact Author)

MIT Connection Science ( email )

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