Computer vision applications in the medical domain have recently become quite popular. Detecting, localizing, and diagnosing diseases and recognizing structures on MRI, CT, PET, XRay, ultrasound and photographic images is efficiently done by AI. What is rarely discussed, is how these AI systems are made. They require copious amounts of training data consisting of images and human-supplied labels. The labels are marked areas (either rectangles or free-form boundaries) that are attached to a word, e.g. “tumor.” Naturally, only highly qualified professionals are able to provide these labels making the process effortful and expensive. A technique called “active learning” from AI can help with this by reducing the manual effort by 90%. This allows the creation of state-of-the-art AI models for medicine using a significantly smaller budget of time and resources. This talk will present the method with several examples and will argue that this approach is a disruptive shift in medical AI.