AI/ML-Enabled Multi-Omics Integration of Host Genetics, Immunity, and the Gut Microbiome in Crohn's Disease: From Diagnosis to Theranostics.

Abstract

Crohn's disease is a long-term inflammatory disorder arising from the interaction of genetic risk factors, immune system dysfunction, and alterations in gut microbiota. Variability in clinical phenotypes and lack of biomarker specificity hinder the efficiency of current traditional diagnostic and treatment approaches. This review aims to assess how AI- and ML-driven multi-omics offer comprehensive insights into pathogenicity, thereby enhancing diagnostic techniques and personalized therapeutic approaches in CD. Current studies employ integration of multi-omics like genomics, proteomics, transcriptomics, metabolomics, and microbiome analysis in CD with AI and ML for significant advancement of biomarker discovery and clinical applications. Emerging evidence reveals that CD is a multi-factorial disorder involving host genetics, immune dysfunction, and microbiome shifts. Integration of advanced AI/ML models with multi-omics data can predict disease-specific biomarkers for easy diagnosis and facilitate precision medicine to enhance therapies. For a successful clinical implementation of an AI/ML model with multi-omics in CD, a standardized data framework and large-scale validation are needed. Additionally, future research should focus on developing interpretable AI models, real-time monitoring systems, and theranostic platforms to enhance precision healthcare delivery.