Artificial Intelligence in Nutrigenomics: A Critical Review on Functional Food Insights and Personalized Nutrition...

This review critically evaluates the applications of artificial intelligence in nutrigenomics, focusing on its role in interpreting functional food-gene interactions, supporting personalized nutrition strategies, and enabling evidence-based dietary interventions for improved health outcomes.

A systematic literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar to identify studies published between 2010 and 2025 addressing AI applications in nutrigenomics and functional foods. Search terms included "artificial intelligence," "nutrigenomics," "personalized nutrition," and "functional foods." Retrieved records were screened for relevance, methodological rigor, and thematic alignment. Following title, abstract, and full-text screening based on predefined inclusion criteria, 176 articles were assessed in detail, and 142 studies were included in the qualitative synthesis. Data were extracted and synthesized to identify key trends, methodological approaches, research gaps.

Artificial intelligence (AI) is increasingly transforming nutrigenomics by enabling personalized dietary recommendations based on genetic, metabolic, and lifestyle data. Machine learning and deep learning approaches facilitate the identification of complex gene-diet interactions, thereby improving the prediction of metabolic and disease-related outcomes. AI-based models support biomarker discovery, genotype-informed dietary guidance, and real-time monitoring through wearable and glucose-monitoring technologies, contributing to improved management of obesity, diabetes, and cardiovascular disorders. These tools enhance understanding of individual variability in dietary response and support precision nutrition strategies.

Despite challenges related to algorithmic bias, data privacy, and ethical governance, AI-driven nutrigenomics offers significant potential to advance personalized nutrition. Continued methodological refinement and responsible implementation are crucial for translating these innovations into clinically meaningful and equitable health applications.