511 citations · 1k across the 4 of their papers we have counts for
4 papers
Closing the AI generalization gap by adjusting for dermatology condition distribution differences across clinical settings
Rajeev V. Rikhye, Aaron Loh, Grace Eunhae Hong +23
Recently, there has been great progress in the ability of artificial intelligence (AI) algorithms to classify dermatological conditions from clinical photographs. However, little i…
Development and Clinical Evaluation of an AI Support Tool for Improving Telemedicine Photo Quality
Kailas Vodrahalli, Justin Ko, Albert S. Chiou +7
Telemedicine utilization was accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, the quality of photographs sent by patients remains a ma…
Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set
Roxana Daneshjou, Kailas Vodrahalli, Roberto A Novoa +16
Access to dermatological care is a major issue, with an estimated 3 billion people lacking access to care globally. Artificial intelligence (AI) may aid in triaging skin diseases.…
Disparities in Dermatology AI: Assessments Using Diverse Clinical Images
Roxana Daneshjou, Kailas Vodrahalli, Weixin Liang +14
More than 3 billion people lack access to care for skin disease. AI diagnostic tools may aid in early skin cancer detection; however most models have not been assessed on images of…