Authors
Saloni Joshi, Akanksha Dwivedi, Arun Kumar, Krishna Aayush, Bhawna Bisht, Sanjay Kumar, Vinod Kumar
Published in
Journal of food science. Volume 91. Issue 7. Pages e71281.
Abstract
Personalized nutrition (PN) has evolved as a novel approach that aims to maintain or enhance health by utilizing genetic, phenotypic, medical, nutritional, and other relevant individual-specific information to deliver tailored dietary guidance and nutrition-related services. The rapid expansion of high-throughput biological data and digital health technologies has positioned artificial intelligence (AI) as a pivotal tool for delivering PN advice that can reach and benefit large populations. This review highlights the role of AI in enabling data-driven PN interventions. AI methodologies that include machine learning, deep learning, natural language processing, and reinforcement learning are discussed in the context of their ability to integrate and analyze complex multimodal health data derived from omics, wearable devices, and electronic health records. The potential of AI to enhance disease prevention and management, as well as real-time nutrition monitoring, is emphasized. Furthermore, the review highlights major challenges that currently limit the widespread adoption of AI-driven nutritional recommendations. This review presents a comprehensive overview of AI-driven approaches in PN while highlighting future opportunities, including multi-omics integration and explainable AI models to transform dietary guidance.
PMID:
42422973
Bibliographic data and abstract were imported from PubMed on 09 Jul 2026.
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