  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.
