AI-Driven Personalized Nutrition: Integrating Omics, Ethics, and Digital Health

Personalized nutrition (PN) aims to prevent and manage chronic diseases by providing individualized dietary guidance based on genetic, metabolic, and lifestyle data. Artificial intelligence (AI) has become a key enabler in PN by analyzing large-scale, multiomics datasets in obesity, diabetes, cardiovascular, and gastrointestinal disorders, where digital twins and health knowledge graphs support personalized interventions. Current findings demonstrate that AI models can guide microbiome-based dietary interventions, and support obesity management, thereby extending the scope of conventional nutritional strategies as supported by deepened bibliometric analyses. This study highlights the global increase in AI-based PN studies, accelerated by digital health demands and the COVID-19 pandemic, and the expansion of traditional nutrition strategies through machine learning approaches with the integration of microbiome-based models and omics. However, challenges such as algorithmic bias, limited generalizability, and data privacy remain. To overcome these issues, diverse datasets, explainable AI approaches, and standardized multicenter validation protocols are proposed. These steps are critical for transforming AI-supported PN from a conceptual potential into a fair, reliable, and clinically applicable structure. The growing consensus in the literature highlights that AI can support individual and societal health goals by transforming nutrition science through predictive, adaptive, and ethically based approaches.