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

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 gene...

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Veröffentlicht in:	arXiv.org 2024-02
Hauptverfasser:	Rikhye, Rajeev V, Loh, Aaron, Hong, Grace Eunhae, Singh, Preeti, Smith, Margaret Ann, Muralidharan, Vijaytha, Wong, Doris, Sayres, Rory, Phung, Michelle, Betancourt, Nicolas, Fong, Bradley, Sahasrabudhe, Rachna, Khoban Nasim, Eschholz, Alec, Mustafa, Basil, Freyberg, Jan, Spitz, Terry, Matias, Yossi, Corrado, Greg S, Chou, Katherine, Webster, Dale R, Bui, Peggy, Liu, Yuan, Liu, Yun, Ko, Justin, Lin, Steven
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial intelligence Dermatology Training
Online-Zugang:	Volltext
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