Head CT is used worldwide to assess neurological emergencies and detect acute brain haemorrhages. Interpreting these head CT scans requires readers to identify tiny subtle abnormalities, with near-perfect sensitivity, within a 3D stack of greyscale images characterized by poor soft-tissue contrast, low signal-to-noise ratio and a high incidence of artefacts. As such, even highly trained experts may miss subtle life-threatening findings. To increase the efficiency, and potentially also the accuracy, of such image analysis, scientists at UC San Francisco (UCSF) and UC Berkeley have developed a fully convolutional neural network, called PatchFCN, that can identify abnormalities in head CT scans with comparable accuracy to highly trained radiologists. Importantly, the algorithm also localizes the abnormalities within the brain, enabling physicians to examine them more closely and determine the required therapy (PNAS 10.1073/pnas.1908021116).