Machine learning in healthcare: Mirage or miracle for breaking the costs dead-lock?

Abstract

The ageing population in all developed economies and the limited productivity characterizing the healthcare sector are leading to alarmingly increasing costs. The current rapid advances in machine learn-ing (ML), a subfield of artificial intelligence (AI), offer new automation and prediction capabilities that could, if properly integrated, help address the healthcare costs deadlock. Are ML-driven solutions the ap-propriate ingredient to produce this necessary transformation, or are they condemned to face the same destiny as previous attempts to remodel healthcare delivery? This paper aims at bringing first elements to answer this question by providing both qualitative and quantitative evidence on the development of ML in healthcare and discussing the organizational and institutional conditions for the ML potential to be real-ized. Building on a novel search methodology for publications and patents in ML and on hospital surveys, our results reveal two major observations. On the one hand, while the publication rate in the field has tripled in the last decade, the level of patenting in ML applied to healthcare has so far been relatively low. This result has several potential explanations, such as the early stage of the technology, its rapid growth, and the emergence of new business models based on data accumulation and appropriation rather than patenting. On the other hand, the bulk of firms’ publications are produced by IT firms rather than by com-panies in healthcare. This last observation seems to be driven by the disruptiveness of the new ML tech-nology allowing the entry of new actors in healthcare. The technology producers benefit from their mas-tery of ML and the lack of investment and capabilities among health experts.

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