Hypothesis Open Access

Artificial Intelligence in Dietetics: A Hypothesis for Personalized, Predictive, and Preventive Nutrition Care

Abstract

The integration of artificial intelligence (AI) into dietetics has the potential to revolutionize nutritional assessment, intervention, and long-term health management. Traditional dietary practices often rely on generalized recommendations that may not account for individual variability in genetics, metabolism, lifestyle, and microbiome composition. This hypothesis article proposes that AI-driven systems can enable a paradigm shift toward highly personalized, predictive, and preventive nutrition care. By integrating data from wearable devices, genomic profiling, dietary patterns, and clinical records, AI can generate precise dietary recommendations tailored to individual needs. Furthermore, AI may enhance early detection of nutrition-related diseases, improve adherence to dietary interventions, and support clinical decisionmaking. However, challenges related to data privacy, algorithmic bias, and implementation must be addressed. This article explores the conceptual framework, potential applications, limitations, and future directions of AI in dietetics, emphasizing its transformative potential in modern healthcare.

Liam O’Connor

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