09/04/2025
๐๐-๐๐ซ๐ข๐ฏ๐๐ง ๐๐๐ซ๐ฌ๐จ๐ง๐๐ฅ๐ข๐ณ๐๐ ๐๐ฎ๐ญ๐ซ๐ข๐ญ๐ข๐จ๐ง ๐๐จ๐ซ ๐๐๐ญ๐๐๐จ๐ฅ๐ข๐ ๐๐๐ซ๐: ๐ ๐๐๐ซ๐ฌ๐ฉ๐๐๐ญ๐ข๐ฏ๐ ๐จ๐ง ๐๐ช๐ฎ๐ข๐ญ๐๐๐ฅ๐ ๐๐ข๐ ๐ข๐ญ๐๐ฅ ๐๐๐๐ฅ๐ญ๐ก ๐๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง๐ฌ
๐Source: Journal of Food Science & Nutrition | ISSN: 2470-1076
๐Published Date: Aug 20, 2025
Metabolic diseases, including and , pose a growing public health and economic burden worldwide. Conventional dietary guidelines often fail to address individual nutritional needs or to achieve adherence in resource-constrained settings.
This Perspective highlights an integrative framework that leverages Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and federated learning to enable culturally sensitive, privacy-preserving, and scalable dietary interventions. Building on prior simulation studies demonstrating over 80% compliance with clinical nutrition targets such as DASH and EASD guidelines, this approach supports real-time, explainable nutrition guidance through virtual nutritionist systems.
The framework aligns with clinical nutrition practices, emphasizes sustainable and localized dietary strategies, and has the potential to enhance equity by extending evidence-based nutritional support to underserved populations. Future real-world validation and long-term studies are needed to assess the public health impact of such -driven systems.
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Figure: A sociotechnical framework for equitable, personalized nutrition in metabolic care. ๐