ABSTRACT Artificial intelligence (AI) is rapidly transitioning from innovation to routine clinical application in dermatology. This review examines how AI‐enabled technologies are being developed and integrated across diverse clinical purposes and workflows. Using a structured assessment template, we analyzed international initiatives and industry‐led innovations to identify the clinical gaps addressed, underlying technologies, use cases, and real‐world implementation experiences. The resulting profiles highlight a broad spectrum of AI‐driven and enabled applications, including wearable sensors with haptic feedback for objective symptom monitoring; patient‐initiated teledermatology platforms that enhance access to specialist care; non‐invasive diagnostic tools employing impedance spectroscopy; autonomous triage systems for dermoscopic lesions; and sensors that combine intrinsic skin biomarkers with exposome analytics. Collectively, these technologies illustrate a shift toward generating objective, reproducible data that complement clinical assessment, facilitating earlier detection, streamlined referrals, and longitudinal, patient‐centred care. While validation studies are encouraging, regulatory and reimbursement pathways, along with limited data diversity, are current hurdles to a more large‐scale adoption. By synthesizing insights from these technological approaches, this review aims to provide dermatologists with a pragmatic overview of AI‐driven health technologies as emerging, evidence‐based, commercial initiatives that may shape the future of dermatologic care.
Crest et al. (Thu,) studied this question.