In traditional ML, models learn from hand-engineered features informed by existing domain knowledge. More recently, in the deep learning era, combining large-scale model architectures, compute, and datasets has enabled learning directly from raw data, often at the expense of human interpretability. In this talk, I'll discuss using deep learning to predict patient outcomes with interpretability methods to extract new knowledge that humans could learn and apply. This process is a natural next step in the evolution of applying ML to problems in medicine and science, moving from the use of ML to distill existing human knowledge to people using ML as a tool for knowledge discovery.