Physicians are still taught to diagnose patients according to the 19th-century Oslerian blueprint. A physician takes a history, performs an examination, and matches each patient to the traditional taxonomy of medical conditions. Symptoms, signs, family history, and laboratory reports are interpreted in light of clinical experience and scholarly interpretation of the medical literature. However, diagnosis is evolving from art to data-driven science, whereby large populations contextualize each individual’s medical condition. Advances in artificial intelligence now bring insight from population-level data to individual care; a recent study sponsored by and including researchers from Google used data sets with more than 11 000 retinal fundus images to develop a deep learning algorithm that outperformed clinicians for detecting diabetic retinopathy.1