AI regulatory strategies: a global public health perspective

Artificial intelligence (AI) has many different potential applications for healthcare system delivery, from imagery testing, triage, to the early detection of infectious patterns in large populations. Scientific literature shows that policies, legislation, and quasi-legislation on the question of AI are rapidly emerging and use one of four main regulatory strategies. Each strategy has limitations and can contribute to the creation of ethical and medico-legal risk, which our research contributes to identify.

We undertook a double-method literature review, first with a scoping literature review. We identified over 1200 documents from ~71 countries (from every continent), as well as international organizations that had adopted a form of regulatory strategy toward AI. We then proceeded to a narrative literature review where we highlighted both the taxonomy of regulatory strategies in use, and their respective risks and challenges to public health objectives.

The legal or quasi-legal framework surrounding the use of AI follows one of four possible strategies: trustworthiness, risk-reduction, ethical/principled, or indirect. The vast majority of the documents surveyed reveal that values and principles of public health are not sufficiently, or even explicitly, built-in the normative framework for health applications of AI (Health AI), and creates a wide array of challenges and risks, which our research helps to identify.

This leads to a detrimental situation and leads to two main consequences: the creation of a "regulatory gap" for Health AI, and also a potential mismatch with already existing medico-legal duties of public health actors and health professionals.