Clinical Implications and Challenges of Artificial Intelligence and Deep Learning

Abstract

Artificial intelligence (AI) and deep learning are entering the mainstream of clinical medicine. For example, in December 2016, Gulshan et al¹ reported development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. An accompanying editorial by Wong and Bressler² pointed out limits of the study, the need for further validation of the algorithm in different populations, and unresolved challenges (eg, incorporating the algorithm into clinical work flows and convincing clinicians and patients to “trust a ‘black box’”). Sixteen months later, the Food and Drug Administration (FDA)³ permitted marketing of the first medical device to use AI to detect diabetic retinopathy. FDA reduced the risk of releasing the device by limiting the indication for use to screening adults who do not have visual symptoms for greater than mild retinopathy, to refer them to an eye care specialist.