AI-Skin Diseases
A new paradigm of physician-AI collaboration in skin disease diagnosis:
multicenter validation and clinical enhancement via a novel multimodal deep learning model
Skin Diseases: Skin diseases are structural and functional abnormalities of the skin caused by diverse endogenous and exogenous factors, ranging from mild epidermal changes to deep tissue or systemic manifestations. Clinically, they appear as primary or secondary lesions, with morphology and progression reflecting underlying etiologies and pathogenic mechanisms. Histopathologically, they involve dysregulated cell proliferation and apoptosis, inflammation, extracellular matrix remodeling, and vascular or neural alterations. As visible indicators of dermatologic and systemic conditions, skin diseases are key targets for diagnosis, monitoring, prognostic evaluation, and therapeutic research.
Dermoscopy images: Dermoscopy images are high-resolution visualizations of the skin surface and superficial dermis obtained using a dermoscope. They reveal subtle features invisible to the naked eye, such as pigment networks, vascular patterns, keratin plugs, and focal pigmentation. Dermoscopy aids in distinguishing benign from malignant lesions and supports quantitative analysis of inflammatory, pigmentary, and other pathological skin conditions.
Clinical images: Clinical images are standard photographs of the skin captured under controlled conditions, depicting lesion size, shape, color, distribution, and relation to surrounding anatomy. They provide broader visual context than dermoscopy, support routine assessment, documentation, longitudinal monitoring, and patient management, and serve as crucial data for AI-based lesion classification, severity grading, and disease progression analysis.
Objectives: This platform aims to develop a clinically oriented AI model for high-precision diagnosis of skin diseases using dermoscopy and clinical images. It explores a novel physician-AI collaboration paradigm and evaluates the model’s generalization across multicenter datasets. Explainable analysis provides scientific support for clinical decision-making, advancing the application of intelligent skin disease diagnostics.
