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.

Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy.

Purpose: To understand the impact of deep learning diabetic retinopathy (DR) algorithms on physician readers in computer-assisted settings.

Design: Evaluation of diagnostic technology.

Participants: One thousand seven hundred ninety-six retinal fundus images from 1612 diabetic patients.