Abstract
Chronic inflammatory skin diseases, including atopic dermatitis, psoriasis, and hidradenitis suppurativa, are challenging to manage due to clinical heterogeneity, subjective assessments, and variable treatment responses. Artificial intelligence (AI) offers transformative potential in addressing these challenges. The review presents an extensive summary of AI applications in chronic skin disease management, covering diagnosis, severity assessment, personalized therapy, and drug discovery, while highlighting validated advances, ongoing gaps, and strategies for clinical implementation. A systematic search of PubMed, Scopus, and Web of Science (2018-2025) prioritized studies with clinical validation, regulatory approval, or prospective trial evidence. AI performs comparably to dermatologists in diagnosing inflammatory skin diseases, with CNNs achieving 85-95% accuracy for psoriasis and atopic dermatitis. Automated EASI and PASI scoring correlates strongly with expert ratings (ICC >0.85). Multi-omics integration enables molecular endotyping of treatment responses (75-85% accuracy). AI also accelerates drug discovery, reducing timelines from years to months. Key challenges include limited diversity in training data, need for multicenter validation, and evolving regulatory frameworks. AI is changing the nature of chronic skin disease management more radically, but the division between technical performance and clinical application is still significant. To close this gap, various datasets have to be created, new validation procedures should be standardized, and the regulation is to be explicit. The introduction of AI into daily dermatology care is likely to provide more accurate, patient-centered, and equitable care.