Deep learning and other forms of machine learning are getting a lot of press in medicine. The reality doesn't match the hype, and interpretable statistical models still have a lot to offer.
I discussed the many advantages or probability estimation over classification. Here I discuss a particular problem related to classification, namely the harm done by using improper accuracy scoring rules. Accuracy scores are used to drive feature selection, parameter estimation, and for measuring predictive performance on models derived using any optimization algorithm.
Optimum decision making in the presence of uncertainty comes from probabilistic thinking. The relevant probabilities are of a predictive nature: P(the unknown given the known). Thresholds are not helpful and are completely dependent on the utility/cost/loss function.
It is important to distinguish prediction and classification. In many decisionmaking contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions.