AI-Driven Genomic and Proteomic Insights in Dermatological Disorders

Abstract

Dermatological disorders represent a massive global health burden, affecting approximately 2 billion people, yet conventional diagnosis often relies on morphology, leading to unpredictable, “trial-and-error” treatment. This necessitates a shift toward precision dermatology, leveraging the genomic and proteomic data that reveal fundamental molecular pathology. However, the sheer volume, high dimensionality, and heterogeneity of multi-omics data (NGS, MS) overwhelm traditional bioinformatics methods. Artificial intelligence (AI), specifically deep learning (DL) and advanced frameworks such as graph neural networks, serves as the transformative computational engine. AI enables high-fidelity protein identification (e.g., Prosit), enhanced biomarker stratification, accurate prediction of gene–environment interactions, and systems-level modeling for therapeutic target discovery. By integrating multi-omics data, AI refines patient stratification and facilitates predictive drug-response modeling, accelerating clinical translation. However, widespread clinical adoption is hampered by significant ethical and technical challenges, including the risk of algorithmic bias due to underrepresentation of diverse skin tones in training datasets, and critical concerns regarding data privacy and the need for regulatory certainty. Future directions center on collaborative approaches such as federated learning and advanced computation such as quantum computing to ensure equitable, transparent, and truly personalized skin healthcare.