Ignoring the mirage of the disposable clinician for the successful deployment of AI in medicine
Medicine is driving many investigators from the machine learning community to the exciting opportunities presented by applying their methodological tool kits to improve patient care. They are inspired by the impressive successes in image analysis (e.g. in radiology, pathology and dermatology) to proceed to broad application to decision support across the time series of patient encounters with healthcare. I will describe the central role of the clinician in this process, and I will examine closely some of the under-appreciated assumptions in that research/engineering agenda. Lastly, I focus on how ignoring these will limit success in medical applications and conversely how these assumptions define a necessary and ambitious research program in shared human-ML decision making.