This document summarizes a study on developing a deep learning system (DLS) for differential skin disease diagnosis using teledermatology data. The DLS was trained on a dataset of 14,000 skin disease cases labeled by 43 dermatologists. It achieved average sensitivity of 80% on validation data, outperforming dermatologists and other medical professionals. Subgroup analysis found the DLS was better at distinguishing malignant, infectious, and non-infectious diseases requiring different treatments. Integrated gradients helped explain the model's decisions. Clinical metadata, like self-reported symptoms, also improved performance. In conclusion, the DLS shows promise as a diagnostic tool for common skin diseases.