no code implementations • 23 Feb 2024 • 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
Recently, there has been great progress in the ability of artificial intelligence (AI) algorithms to classify dermatological conditions from clinical photographs.
no code implementations • 15 Mar 2022 • Roxana Daneshjou, Kailas Vodrahalli, Roberto A Novoa, Melissa Jenkins, Weixin Liang, Veronica Rotemberg, Justin Ko, Susan M Swetter, Elizabeth E Bailey, Olivier Gevaert, Pritam Mukherjee, Michelle Phung, Kiana Yekrang, Bradley Fong, Rachna Sahasrabudhe, Johan A. C. Allerup, Utako Okata-Karigane, James Zou, Albert Chiou
To ascertain potential biases in algorithm performance in this context, we curated the Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones.
no code implementations • 15 Nov 2021 • Roxana Daneshjou, Kailas Vodrahalli, Weixin Liang, Roberto A Novoa, Melissa Jenkins, Veronica Rotemberg, Justin Ko, Susan M Swetter, Elizabeth E Bailey, Olivier Gevaert, Pritam Mukherjee, Michelle Phung, Kiana Yekrang, Bradley Fong, Rachna Sahasrabudhe, James Zou, Albert Chiou
AI diagnostic tools may aid in early skin cancer detection; however most models have not been assessed on images of diverse skin tones or uncommon diseases.