PRECISION NUTRITION IN THE ERA OF ARTIFICIAL INTELLIGENCE: FROM PERSONALIZED DIETS TO POPULATION HEALTH

Abstract

Precision nutrition is an emerging approach that uses individual biological, behavioral, environmental, and lifestyle characteristics to develop personalized dietary strategies for improving health outcomes. The rapid advancement of artificial intelligence (AI) has created new opportunities to transform precision nutrition by enabling the analysis of complex and large-scale datasets, including genetic information, metabolic profiles, dietary patterns, physical activity, and health records. This paper explores the role of AI in advancing precision nutrition from personalized diets to broader population health applications. AI-driven tools can support individualized dietary recommendations, predict nutritional risks, identify disease patterns, and assist in the prevention and management of obesity, diabetes, cardiovascular disease, and other nutrition-related conditions. At the population level, AI can help identify vulnerable groups, monitor dietary trends, and inform targeted public health interventions and policies. However, challenges related to data privacy, algorithmic bias, unequal access to technology, data quality, and ethical considerations may limit its effective implementation. Integrating AI with nutrition science requires multidisciplinary collaboration, strong regulatory frameworks, and evidence-based validation. The responsible use of AI in precision nutrition has the potential to bridge personalized care and population health, promoting more effective, equitable, and sustainable strategies for improving nutritional well-being and reducing the global burden of diet-related diseases.