Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions.

Supervised deep learning models have proven to be highly effective in classification of dermatological conditions. These models rely on the availability of abundant labeled training examples. However, in the real-world, many dermatological conditions are individually too infrequent for per-condition classification with supervised learning.

Development and Assessment of an Artificial Intelligence-Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology Practices.

Importance: Most dermatologic cases are initially evaluated by nondermatologists such as primary care physicians (PCPs) or nurse practitioners (NPs).

Objective: To evaluate an artificial intelligence (AI)-based tool that assists with diagnoses of dermatologic conditions.

Design, Setting, And Participants: This multiple-reader, multiple-case diagnostic study developed an AI-based tool and evaluated its utility.

A deep learning system for differential diagnosis of skin diseases.

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy.

Automated audiometry using apple iOS-based application technology.

Objective: The aim of this study is to determine the feasibility of an Apple iOS-based automated hearing testing application and to compare its accuracy with conventional audiometry.

Study Design: Prospective diagnostic study. Setting Academic medical center.