In the past few years we have seen many interesting applications of AI in mitigating underdiagnoses in imaging. Most methods, including those that rely on deep learning, use very different algorithms than radiologists use intellectually. Furthermore, whereas human radiologists can add intuition, flexibility, and creativity in diagnosis, computer algorithms give consistency, resistance to fatigue, and instant availability day or night. So for the foreseeable future, radiologists and algorithms will complement each other. Neither will be 100 percent perfect, but together we expect diagnosis to grow even more accurate and, hopefully, more efficient. Humans will, of course, need to adjudicate results, to sort through the wealth of information algorithms will generate to reject what is clearly inaccurate while promoting what is plausible. AI will also reduce some of the mundane human tasks: Summarizing data that is easily retrieved and processed, and formatting draft results into systematic, consistent, and readable form. This will allow the human to concentrate on the more intellectual tasks of detection and description of an abnormality while providing thoughtful diagnosis.