This paper considers the use of machine learning in medicine by focusing on the main problem that it has been aimed at solving or at least minimizing: uncertainty. However, 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 this class of computational models, thus undermining the clinical significance of their output. Recognizing this can motivate researchers to pursue different ways to assess the value of these decision aids, as well as alternative techniques that do not “sweep uncertainty under the rug” within an objectivist fiction (which doctors can come up by trusting).
Cabitza, F., Ciucci, D., Rasoini, R. (2019). A giant with feet of clay: On the validity of the data that feed machine learning in medicine. In F. Cabitza, C. Batini, M. Magni (a cura di), Organizing for the Digital World: IT for Individuals, Communities and Societies (pp. 121-136). Springer Heidelberg [10.1007/978-3-319-90503-7_10].
A giant with feet of clay: On the validity of the data that feed machine learning in medicine
Cabitza, F;Ciucci, D
;
2019
Abstract
This paper considers the use of machine learning in medicine by focusing on the main problem that it has been aimed at solving or at least minimizing: uncertainty. However, 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 this class of computational models, thus undermining the clinical significance of their output. Recognizing this can motivate researchers to pursue different ways to assess the value of these decision aids, as well as alternative techniques that do not “sweep uncertainty under the rug” within an objectivist fiction (which doctors can come up by trusting).
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