Due to a convergence of the availability of large datasets, graphics-specific computer hardware, and important theoretical advancements, artificial intelligence (AI) has recently contributed to dramatic progress in medicine. One type of artificial intelligence known as deep learning (DL) 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 in order to effectively use new applications as well as to better gauge their utility and limitations. In this second article of a two 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.