Modeling human skin aging with artificial intelligence: from multi-layer structure to personalized intervention

Abstract Skin aging is driven by progressive alterations across cutaneous structures and systemic biological processes, yet current assessment and intervention strategies remain largely population based and lack precision. Recent advances in artificial intelligence (AI) enable integrated modeling of skin aging phenotypes, structural dynamics, and individualized rejuvenation responses through multimodal analysis and predictive simulation. Here, we summarize emerging AI-driven approaches in skin aging research, focusing on multi-structure modeling, multimodal data integration, interpretable prediction, and clinically closed-loop optimization. We further discuss current challenges in data heterogeneity, generalizability, fairness, and clinical translation, and outline future directions toward personalized rejuvenation medicine.