Robert Sparrow

Robert Sparrow, Joshua Hatherley

In the much-celebrated book Deep Medicine, Eric Topol argues that the development of artificial intelligence for health care will lead to a dramatic shift in the culture and practice of medicine. In the next several decades, he suggests, AI will become sophisticated enough that many of the everyday tasks of physicians could be delegated to it. Topol is perhaps the most articulate advocate of the benefits of AI in medicine, but he is hardly alone in spruiking its potential to allow physicians to dedicate more of their time and attention to providing empathetic care for their patients in the future. Unfortunately, several factors suggest a radically different picture for the future of health care. Far from facilitating a return to a time of closer doctor-patient relationships, the use of medical AI seems likely to further erode therapeutic relationships and threaten professional and patient satisfaction.

Joshua Hatherley, Robert Sparrow

Objectives: Machine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this paper, we provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine that have, thus far, been neglected in the literature. Target audience: The target audiences for this tutorial are the developers of machine learning AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. Scope: Discussions of adaptive ML systems to date have overlooked the distinction between two sorts of variance that such systems may exhibit -- diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites) -- and under-estimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems.