511 citations · 1k across the 6 of their papers we have counts for
6 papers
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis
Roxana Daneshjou, Mert Yuksekgonul, Zhuo Ran Cai +2
For the deployment of artificial intelligence (AI) in high-risk settings, such as healthcare, methods that provide interpretability/explainability or allow fine-grained error analy…
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…
TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos
Kailas Vodrahalli, Roxana Daneshjou, Roberto A Novoa +3
Telehealth is an increasingly critical component of the health care ecosystem, especially due to the COVID-19 pandemic. Rapid adoption of telehealth has exposed limitations in the…
Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning
Yunzhu Li, Andre Esteva, Brett Kuprel +3
Dense object detection and temporal tracking are needed across applications domains ranging from people-tracking to analysis of satellite imagery over time. The detection and track…