3 challenges for artificial intelligence in medicine

In a deep learning representation of human disease, lower layers could represent clinical measurements (such as ECG data or protein biomarkers), intermediate layers could represent aberrant pathways (which may simultaneously impact many biomarkers), and top layers could represent disease subclasses (which arise from the variable contributions of 1 aberrant pathways). Ideally, such subclasses would do more than stratify by risk and would actually reflect the dominant disease mechanism(s). This raises a question about the underlying pathophysiologic basis of complex disease in any given individual: is it sparsely encoded in a limited set of aberrant pathways, which could be recovered by an unsupervised learning process (albeit with the right features collected and a large enough sample size), or is it a diffuse, multifactorial process with hundreds of small determinants combining in a highly variable way in different individuals? In the latter case, the concept of precision medicine is unlikely to be of much utility. However, in the former situation, unsupervised and perhaps deep learning might actually realize the elusive goal of reclassifying patients according to more homogenous subgroups, with shared pathophysiology, and the potential of shared response to therapy.