Closing the AI generalization gap by adjusting for dermatology condition distribution differences across clinical settings

arXiv:2402.15566v1 Announce Type: cross
Abstract: Recently, there has been great progress in the ability of artificial intelligence (AI) algorithms to classify dermatological conditions from clinical photographs. However, little is known about the robustness of these algorithms in real-world settings where several factors can lead to a loss of generalizability. Understanding and overcoming these limitations will permit the development of generalizable AI that can aid in the diagnosis of skin conditions across a variety of clinical settings. In this retrospective study, …

abstract adjusting algorithms artificial artificial intelligence arxiv clinical cs.cv cs.lg dermatology differences distribution eess.iv gap intelligence photographs progress robustness type world