AI-Driven Predictive Modeling for Personalized Vitiligo Treatment and Patient Stratification

Abstract

Vitiligo is hard to personalize because disease activity, anatomical response, immune persistence, and repigmentation capacity vary widely across patients. This chapter proposes a precision-medicine framework integrating longitudinal clinical data, standardized imaging, patient-reported outcomes, biomarkers, transcriptomics, and candidate pharmacogenomic features to forecast response and stratify patients. Its architecture combines multimodal representation learning, time-to-event modeling, causal treatment-effect estimation, uncertainty quantification, and explainable decision support. The framework separates validated predictors from exploratory genomic candidates and does not suggest genotype-guided prescribing is yet established in vitiligo. It also defines data standards, treatment-specific endpoints, external validation procedures, fairness safeguards across skin tones, and staged implementation steps. Finally, it outlines priorities for biomarker qualification, adaptive trials, federated learning, and responsible translation into dermatology for diverse populations.