Artificial intelligence for decoding diet-gut microbiome-health interactions: A systematic scoping review
Diet is a major modifiable determinant of metabolic, immune, and inflammatory health, yet individual responses to the same dietary exposure often differ markedly because many effects are mediated by the gut microbiome. This systematic scoping review summarizes how artificial intelligence (AI) and machine learning (ML) have been used to decode diet-gut microbiome-health interactions. Following PRISMA 2020 guidance, we searched PubMed, Web of Science, Scopus, IEEE Xplore, Google Scholar, and citation lists for studies linking diet or nutrition, gut microbiome data, AI/ML methods, and health-related outcomes. Evidence was organized across biological mechanisms, data types, algorithm families, validation strategies, translational applications, and ethical constraints. Current evidence shows that tree-based ensembles, penalized regression, neural networks, autoencoders, graph learning, explainable AI, and emerging foundation-model approaches can improve phenotype prediction, multi-omics integration, microbial feature discovery, and personalized dietary response modeling. However, most studies remain limited by small or geographically restricted cohorts, heterogeneous sequencing and dietary assessment protocols, incomplete external validation, and weak experimental confirmation of model-derived mechanisms. We therefore propose a validation-to-translation framework that links leakage-free model development, cohort-external testing, explainable biological interpretation, prospective dietary intervention, privacy-preserving data sharing, and regulatory evaluation. AI is unlikely to replace mechanistic microbiome research, but it can substantially strengthen precision nutrition when embedded in transparent, reproducible, and clinically validated workflows.
Authors
He, Zichen; Zhou, Fangzheng; Hao, Yanling; Fan, Bei; Tong, Jixu; Guo, Zhe; Wang, Fengzhong; Ma, Peihua
Citation
He, Zichen; Zhou, Fangzheng; Hao, Yanling; Fan, Bei; Tong, Jixu; et al. 2026. Artificial intelligence for decoding diet-gut microbiome-health interactions: A systematic scoping review. Food Nutrition 2(2): 100087. https://doi.org/10.1016/j.fnutr.2026.100087
Keywords
Artificial Intelligence; Digital Technology; Diet; Microbiomes; Health; Models; Microbial Properties
Record type
Journal Article
