A deep learning system for differential diagnosis of skin diseases

Skin and subcutaneous conditions affect an estimated 1.9 billion people at any given time and remain the fourth leading cause of non-fatal disease burden worldwide. Access to dermatology care is limited due to a shortage of dermatologists, causing long wait times and leading patients to seek dermatologic care from general practitioners. However, the diagnostic accuracy of general practitioners has been reported to be only 0.24-0.70 (compared to 0.77-0.96 for dermatologists), resulting in over and under referrals, delays in care, and errors in diagnosis and treatment. In this paper, we developed a deep learning system (DLS) to provide clinical cases (skin photographs and associated medical histories) with a differential diagnosis across 26 of the most common skin conditions, representing roughly 80% of the volume of skin conditions seen in a primary care setting. The DLS was developed and validated using de-identified cases from a teledermatology practice via a temporal split: the first 14,021 cases for development and the last 3,756 cases for validation. On the validation set, where a panel of three board-certified dermatologists defined the reference standard for every case, the DLS achieved 0.71 and 0.93 top-1 and top-3 accuracies, respectively. For a subset of n=963 cases, three groups of 18 clinicians reviewed the cases for comparison. On this subset, the DLS achieved a 0.67 top-1 accuracy, non-inferior to board-certified dermatologists (0.63), and higher than primary care physicians (PCPs, 0.45) and nurse practitioners (NPs, 0.41). The top-3 accuracy showed a similar trend: 0.90 DLS, 0.75 dermatologists, 0.60 PCPs, and 0.55 NPs. These results highlight the potential of deep learning to help general practitioners diagnose skin disease more accurately by providing them with a candidate differential diagnosis. Future work will be needed to prospectively assess the clinical impact of using this tool in actual clinical workflows.