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The review aims to shift AI in dermatology from simple image classification to comprehensive multimodal clinical reasoning.

June 15, 2026Open Access

Artificial intelligence (AI)-assisted diagnosis of skin diseases: From image classification to dermatology-specific multimodal clinical reasoning

Key Points

The review aims to shift AI in dermatology from simple image classification to comprehensive multimodal clinical reasoning.
Summarized advances in convolutional neural networks and clinical-image datasets.
Analyzed challenges unique to dermatology, including morphologic overlap and skin-tone bias.
Discussed future directions, emphasizing fairness and collaboration between AI and clinicians.
Identified that single photographs are often insufficient for diagnosing complex skin conditions.
Highlighted the need for skin-tone-aware models and enhanced clinical reasoning frameworks.
Outlined a pathway to develop safe, interpretable, and equitable AI systems for skin disease management.

Abstract

Artificial intelligence (AI) in dermatology has moved beyond the early paradigm of single-image classification. Dermatological diagnosis is achieved based on morphology, distribution, symptoms, tactile findings, temporal evolution, patient history, histopathology, and treatment response. Clinically important differentials, such as eczema versus psoriasis, cutaneous T-cell lymphoma versus chronic dermatitis, drug eruption versus viral exanthem, lupus erythematosus versus dermatomyositis, and melanoma versus atypical nevus, are rarely resolved with one photograph alone. This review therefore frames AI-assisted dermatology around a central argument: the field must progress from lesion recognition to dermatology-specific multimodal clinical reasoning. We summarize major advances in convolutional neural networks, dermoscopic benchmarks, clinical-image datasets, large language models, vision-language systems, and dermatology foundation models. We also analyze challenges that are particularly relevant to dermatology, including morphologic overlap, skin-tone bias, reduced erythema visibility on darker skin, dataset imbalance, variable smartphone imaging, imperfect reference standards, and the gap between benchmark performance and clinical deployment. Special attention is given to fairness, regulatory oversight, software as a medical device, human-AI collaboration, prognosis prediction, biologic-response modeling, longitudinal monitoring, and treatment optimization. Finally, we discuss future directions, including skin-tone-aware foundation models, lesion-level and body-site grounding, pathology-genomics integration, dermatology copilots, post-marketing surveillance, and prospective clinical trials. By prioritizing dermatological reasoning rather than generic AI architecture, this review outlines a clinically grounded pathway for building safe, interpretable, equitable, and useful AI systems for skin disease management.

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