  Artificial intelligence (AI) algorithms using deep learning have advanced the
classification of skin disease images; however these algorithms have been
mostly applied "in silico" and not validated clinically. Most dermatology AI
algorithms perform binary classification tasks (e.g. malignancy versus benign
lesions), but this task is not representative of dermatologists' diagnostic
range. The American Academy of Dermatology Task Force on Augmented Intelligence
published a position statement emphasizing the importance of clinical
validation to create human-computer synergy, termed augmented intelligence
(AuI). Liu et al's recent paper, "A deep learning system for differential
diagnosis of skin diseases" represents a significant advancement of AI in
dermatology, bringing it closer to clinical impact. However, significant issues
must be addressed before this algorithm can be integrated into clinical
workflow. These issues include accurate and equitable model development,
defining and assessing appropriate clinical outcomes, and real-world
integration.
