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TRV-2026-1171Certified recordPeer-reviewed

Artificial intelligence in food and nutrition science: a paradigm-centric review of computational frameworks and system-level integration

Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-called macronutrients but rather as dynamic and heterogeneous biochemical matrices. These complex non-linear interactions between food composition, gastrointestinal digestion and the human microbiome are difficult to capture using traditional empirical experimental methods. This review articulates a paradigm-based framework that reconceptualizes artificial intelligence (AI) in food and function science, fr…

Lifestyle · P Space — documented harm · certified 2026-09-22 · v1 · article view · machine-readable

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Finally, we demonstrate how Microbial Community-scale Metabolic Modeling (MCMM) and multimodal machine learning mechanistically couple specific dietary inputs with unique microbiome responses, yielding up to 95% diagnostic accuracy in differentiating diet-responsive metabolic states and individualized postprandial glycemic outcomes.

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Truvace Impact Record TRV-2026-1171, v1: “Artificial intelligence in food and nutrition science: a paradigm-centric review of computational frameworks and system-level integration.” Truvace, 2026-09-22. /record/TRV-2026-1171 (accessed at citation time). sha256 871122727b89eebe…

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