Artificial intelligence driven diet-intestinal microbiota-host health integration: A four-dimensional paradigm for...

The artificial intelligence (AI)-driven diet-intestinal microbiota-host health integration paradigm has emerged as a novel framework for advancing host health research. Intestinal microbiota serves as a key mediator linking dietary signals to host homeostasis, while AI enables efficient integration of multi-omics data to construct microbial metabolic models, identify interaction patterns across biological scales, and support prediction-informed modulation of diet-microbiota interactions. This paradigm synergizes dietary intervention design, AI technology, intestinal microbiota modulation, and host health enhancement, offering innovative solutions for chronic disease management, animal health breeding, and food safety monitoring. In this review, we summarize the core mechanisms of the four-dimensional interaction and the application value of AI-driven informed optimization. This review also discusses the major challenges currently facing the field, including model interpretability, multi-source data heterogeneity, and cross-species translation, and further highlights that future research should focus on establishing interpretable and iterative AI-driven closed-loop systems. This integrated approach holds immense potential to revolutionize host wellness research and promote the development of nutritional health and related industries. Unlike existing diet-microbiota-host frameworks and AI-assisted precision nutrition approaches that mainly focus on association analysis or outcome prediction, our proposed paradigm positions AI as an active coordination layer linking dietary inputs, microbial responses, and host outcomes. By integrating mechanism-informed modeling with iterative feedback, this framework enables dynamic optimization of diet-microbiota interactions and supports more precise host health regulation.