Deep Learning models are well suited to analyze the types of data created in modern medical workflows: raw medical images, annotations, and reports. Through the use of electronic health records, it is possible to use tens of millions of records representing billions of data points without any lapse in attention and apply the models to make predictions in a uniform fashion.
The types of patterns that Deep Learning models are capable of detecting can be very subtle, and go beyond "mere" statistical inference. While training on the data, is is common for the model to recognize patterns in images or text that manifest in the form of shapes, three-dimensional structures, feeling/emotion, or other correlations that can't readily be explained.
The image below shows two samples from a model trained to detect objects in photos and a visualization generated from a library called SHAP (SHapley Additive exPlanations). SHAP is a tool used to explore Deep Learning models and try to understand why they return specific results. The two most likely predictions are shown for images of a dowitcher and meerkat, along with a visualization explaining why those labels were chosen. Red pixels represent positive contributions to a label while blue pixels represent negative contributions. The model has identified features of the two animals which distinguish them from other labels. The dark and slender bill of the dowitcher and the distinctive dark markings of the meerkat are powerful positive contributors for those labels, but are absent from the red backed sand-piper and mongoose (the second most likely labels).