The Journal of American Medical Association (JAMA) published a viewpoint titled "Unintended Consequences of Machine Learning in Medicine" [Cabitza2017JAMA]. The title is eye-catching, and it is an interesting read touching upon several important points of concern to those working at the cross roads of machine-learning (ML) and decision support systems (DSS). This viewpoint is timely, arriving at a time when others are also expressing concern about inflated expectations of machine learning and its fundamental limitations [Chen2017NEJM]. However, several points put forth as alarming in this piece are in my opinion unsupported. In this quick take, I hope to convince you that the reports of unintended consequences specifically due to ML have been greatly exaggerated.

Over the past decade, machine learning techniques have made substantial advances in many domains. In health care, global interest in the potential of machine learning has increased; for example, a deep learning algorithm has shown high accuracy in detecting diabetic retinopathy.1 There have been suggestions that machine learning will drive changes in health care within a few years, specifically in medical disciplines that require more accurate prognostic models (eg, oncology) and those based on pattern recognition (eg, radiology and pathology). However, comparative studies on the effectiveness of machine learning–based decision support systems (ML-DSS) in medicine are lacking, especially regarding the effects on health outcomes. Moreover, the introduction of new technologies in health care has not always been straightforward or without unintended and adverse effects.2 In this Viewpoint we consider the potential unintended consequences that may result from the application of ML-DSS in clinical practice.

This paper considers the use of Machine Learning (ML) in medicine by focusing on the main problem that this computational approach has been aimed at solving or at least minimizing: uncertainty. To this aim, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input of ML models, thus undermining the clinical significance of their output. Recognizing this can motivate both medical doctors, in taking more responsibility in the development and use of these decision aids, and the researchers, in pursuing different ways to assess the value of these systems. In so doing, both designers and users could take this intrinsic characteristic of medicine more seriously and consider alternative approaches that do not "sweep uncertainty under the rug" within an objectivist fiction, which everyone can come up by believing as true.