From analysis of medical imaging such as echocardiograms, computed tomography (CT), endoscopy, and skin photographs, to tissue histology and physiological data such as electrocardiograms (ECG), these technologies have demonstrated enormous potential for health care by helping screen for diseases, classify malignancies, and provide personalized treatment recommendations, often sooner than is possible via standard technologies. At the same time, these products raise unique regulatory questions due to their iterative and potentially self-updating nature, which is incongruent with the U.S. Food and Drug Administration's (FDA or the agency) historical approach of seeking validation of a frozen device design prior to submission to FDA. The use of machine learning offers the opportunity for continual optimization of an algorithm as new training data becomes available; however, this potential has been in tension with FDA's standard policies on when to submit new marketing applications to the agency.