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

• URL: http://arxiv.org/abs/2402.15566v1
• Date: Fri, 23 Feb 2024 19:07:53 GMT
• Title: Closing the AI generalization gap by adjusting for dermatology condition distribution differences across clinical settings
• Authors: Rajeev V. Rikhye, Aaron Loh, Grace Eunhae Hong, Preeti Singh, Margaret Ann Smith, Vijaytha Muralidharan, Doris Wong, Rory Sayres, Michelle Phung, Nicolas Betancourt, Bradley Fong, Rachna Sahasrabudhe, Khoban Nasim, Alec Eschholz, Basil Mustafa, Jan Freyberg, Terry Spitz, Yossi Matias, Greg S. Corrado, Katherine Chou, Dale R. Webster, Peggy Bui, Yuan Liu, Yun Liu, Justin Ko, Steven Lin
• Abstract summary: We show that differences in skin condition distribution are the main source of errors when an AI algorithm is evaluated on data from a previously unseen source. Our results suggest comparable performance from end-to-end fine tuning versus fine tuning solely the classification layer on top of a frozen embedding model.
• Score: 17.345850219146424
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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