Abstract
Skin disorders affect close to one-third of the global population and impose a disability burden that rivals many internal diseases, yet they remain conspicuously under-prioritised in research and health policy. Their biochemical substrate is remarkably heterogeneous, spanning chronic cytokine-driven inflammation in psoriasis and atopic dermatitis, dysregulated sebaceous lipid metabolism and microbial dysbiosis in acne, disordered melanogenesis in vitiligo and melasma, and oncogenic signalling with metabolic reprogramming in keratinocyte and melanocytic cancers. Conventional diagnostics and therapeutics are frequently limited by late detection, the subjective nature of visual and histological assessment, marked inter-individual variability in drug response, and an incomplete understanding of pathway crosstalk. Artificial intelligence (AI), and in particular machine learning and deep learning, offers a route to integrate multimodal biochemical, imaging, and clinical data at a scale and granularity beyond human capacity. This review first synthesises the biochemical hallmarks of the major skin diseases and then critically examines how AI is being deployed to (i) discover and prioritise molecular biomarkers from genomic, transcriptomic, proteomic, and metabolomic datasets; (ii) raise diagnostic accuracy above that of conventional histology and unaided clinicians; (iii) predict individual responses to biologics and small-molecule inhibitors and personalise treatment sequences and (iv) accelerate drug discovery and repurposing for both common and rare dermatoses. We close by appraising the principal obstacles to clinical translation - data heterogeneity and the absence of standardised biochemical ontologies, the opacity of high-capacity models, algorithmic bias across skin phototypes, and regulatory and privacy concerns and outline a path towards an explainable, multimodal, and equitable digital dermatology that genuinely complements biochemical knowledge rather than displacing it