Closing the AI generalization gap by adjusting for dermatology condition distribution differences across clinical settings
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, we demonstrate that differences in skin condition distribution, rather than in demographics or image capture mode are the main source of errors when an AI algorithm is evaluated on data from a previously unseen source. We demonstrate a series of steps to close this generalization gap, requiring progressively more information about the new source, ranging from the condition distribution to training data enriched for data less frequently seen during training. Our results also suggest comparable performance from end-to-end fine tuning versus fine tuning solely the classification layer on top of a frozen embedding model. Our approach can inform the adaptation of AI algorithms to new settings, based on the information and resources available.
Cite
@article{arxiv.2402.15566,
title = {Closing the AI generalization gap by adjusting for dermatology condition distribution differences across clinical settings},
author = {Rajeev V. Rikhye and Aaron Loh and Grace Eunhae Hong and Preeti Singh and Margaret Ann Smith and Vijaytha Muralidharan and Doris Wong and Rory Sayres and Michelle Phung and Nicolas Betancourt and Bradley Fong and Rachna Sahasrabudhe and Khoban Nasim and Alec Eschholz and Basil Mustafa and Jan Freyberg and Terry Spitz and Yossi Matias and Greg S. Corrado and Katherine Chou and Dale R. Webster and Peggy Bui and Yuan Liu and Yun Liu and Justin Ko and Steven Lin},
journal= {arXiv preprint arXiv:2402.15566},
year = {2024}
}