Machine learning for precision dermatology: Advances, opportunities, and outlook

Abstract

To the Editor: With the explosion of big data in medicine driven by the advent of electronic medical records, next-generation sequencing, multi-omics, and noninvasive imaging techniques, dermatology is a field at the precipice of an artificial intelligence (AI) revolution. However, to the majority of clinicians, machine learning (ML) is a magical black box that is powerful but inaccessible. Here, we review the latest advances in ML applied to dermatologic diagnosis and treatment and highlight key discoveries with translational potential. ML is an AI technique that focuses on designing machines (or computers) that mimic human pattern recognition and problem solving.1 With the rise of big data and data science, ML and AI already affect our daily lives in innumerable ways. Comparatively, clinical medicine has been slower to integrate ML into daily practice.2 ML has typically been considered a tool well outside of a typical clinician's purview. At the same time, there is now an enormous demand for high-quality research that is advancing health care using ML and AI.3 ML is a natural fit for translation into dermatology because dermatology is a specialty that is heavily reliant on visual evaluation and pattern recognition.

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