How competitors become collaborators-Bridging the gap(s) between machine learning algorithms and clinicians

Abstract

For some years, we have been witnessing a steady stream of high-profile studies about machine learning (ML) algorithms achieving high diagnostic accuracy in the analysis of medical images. That said, facilitating successful collaboration between ML algorithms and clinicians proves to be a recalcitrant problem that may exacerbate ethical problems in clinical medicine. In this paper, we consider different epistemic and normative factors that may lead to algorithmic overreliance within clinical decision-making. These factors are false expectations, the miscalibration of uncertainties, non-explainability, and the socio-technical context within which the algorithms are utilized. Moreover, we identify different desiderata for bridging the gap between ML algorithms and clinicians. Further, we argue that there is an intriguing dialectic in the collaboration between clinicians and ML algorithms. While it is the algorithm that is supposed to assist the clinician in diagnostic tasks, successful collaboration will also depend on adjustments on the side of the clinician.

Keywords: clinical expertise; defensive medicine; human-computer interaction; machine learning; medical diagnosis.