Health and disease are increasingly understood through the lens of interconnected biological systems shaped by host genetics, microbial ecology, nutritional inputs, metabolic regulation, and environmental exposures. Advances in multi-omics technologies and artificial intelligence have accelerated the ability to examine these systems at high resolution, revealing previously obscured patterns that govern immune equilibrium, metabolic stability, and long-term disease susceptibility.

As global burdens of cancer, diabetes, inflammatory disorders, and environmental toxicity continue to rise, there is an urgent need for integrative frameworks that move beyond reductionist models and toward predictive, preventive, and personalized approaches.

Advancing Personalized Medicine with AI-Driven Microbiome and Nutrigenomics: Innovations and Future Perspectives presents a comprehensive systems-level exploration anchored in the polybiome framework—a model that unifies microbial fermentomics, nutrigenomic modulation, immunometabolic regulation, and environmental interactions.

The volume introduces NexDi, an AI-enabled diabetes reclassification system based on extensive cohort data, illustrating how multi-omics integration can predict metabolic instability and β-cell vulnerability before clinical manifestation. Additional chapters extend this framework to oncological microenvironments, immune–microbial signaling networks, precision nutrition algorithms, engineered microbial consortia, and ethical governance in AI-driven healthcare.

This reference is designed for researchers, clinicians, data scientists, and policymakers seeking rigorous methodologies and translational insights to advance personalized medicine, chronic disease prevention, and biointelligent health systems.