Towards Realization of Augmented Intelligence in Dermatology: Advances and Future Directions
This work is incremental, focusing on advancing AI towards clinical use in dermatology by addressing validation and integration issues.
The paper addresses the gap between AI algorithms for skin disease classification and clinical application, noting that most are not clinically validated and limited to binary tasks, and highlights Liu et al.'s system as a step forward but identifies key challenges like equitable development and real-world integration.
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.