AI in clinical diagnostics in dermatology: applications, validation, and real-world use cases

Artificial intelligence (AI) has moved from proof-of-concept studies in dermatology to selective, real-world clinical use, particularly in image-based triage, lesion assessment, and workflow augmentation. Dermatology is uniquely suited to AI because much of diagnostic reasoning depends on visual information (clinical photos, dermoscopy, reflectance confocal microscopy, optical coherence tomography) plus context (history, distribution, symptoms, treatments, and risk factors). Yet translation into routine care depends less on eye-catching benchmark accuracy and more on careful validation, robust reference standards, fairness across skin tones and devices, usability within clinical workflows, and continuous post-deployment monitoring for performance and safety. This review summarizes leading diagnostic applications of AI in dermatology, provides a practical framework for clinical validation, and highlights real-world deployment patterns including teledermatology triage, melanoma risk workflows, inflammatory dermatoses severity scoring, and AI-assisted dermatopathology. We emphasize that "AI performance" is not a single number: the intended use (screening vs. diagnosis vs. referral prioritization), operating thresholds, prevalence, human-AI interaction, and downstream clinical actions determine real impact. Finally, we outline implementation considerations (governance, privacy, regulatory expectations, and monitoring) and propose practical steps for clinics and health systems seeking safe adoption.