Deep learning for dermatologists: Part II. Current applications

Because of a convergence of the availability of large data sets, graphics-specific computer hardware, and important theoretical advancements, artificial intelligence has recently contributed to dramatic progress in medicine. One type of artificial intelligence known as deep learning has been particularly impactful for medical image analysis. Deep learning applications have shown promising results in dermatology and other specialties, including radiology, cardiology, and ophthalmology. The modern clinician will benefit from an understanding of the basic features of deep learning to effectively use new applications and to better gauge their utility and limitations. In this second article of a 2-part series, we review the existing and emerging clinical applications of deep learning in dermatology and discuss future opportunities and limitations. Part 1 of this series offered an introduction to the basic concepts of deep learning to facilitate effective communication between clinicians and technical experts.

Automated summary

Artificial intelligence, particularly deep learning, has made significant contributions to medical image analysis in dermatology and other specialties such as radiology, cardiology, and ophthalmology. Understanding the basic features of deep learning is crucial for clinicians to effectively use new applications and determine their limitations and utility. This article reviews the applications of deep learning in dermatology and discusses future opportunities and limitations.