A comprehensive review published in Skin outlines the emergence of “Dermatology AI 2.0”, a fundamental transition from pattern recognition to cognitive and actionable intelligence. Built on four core pillars—causal inference, skin digital twins, predictive intervention, and distributed autonomous networks—this new paradigm enables AI to diagnose rare diseases 30% more accurately, predict disease flares with >90% accuracy, and deliver full-lifecycle skin health management. The review emphasizes that AI will not replace clinicians but will automate routine tasks, allowing physicians to focus on complex cases and patient care.

This study establishes a comprehensive three-level data standardization framework for integrating Western medicine (WM) and traditional Chinese medicine (TCM) electronic medical records in psoriasis research. It resolves the “data-rich, information-poor” paradox in dermatology and enables real-world evidence generation and AI-assisted clinical decision support for integrated therapies.