The intersection of artificial intelligence and microbiome science may be one of the most consequential methodological shifts in personalized medicine this decade. For decades, the gut microbiome's staggering complexity — trillions of microorganisms generating multi-dimensional interaction data — has outpaced the analytical tools available to make clinical sense of it. That gap is now narrowing rapidly, with real implications for disease diagnostics and therapeutic targeting.
This systematic review in Gut catalogues how an expanding AI toolkit is being applied to microbiome datasets. Classical machine learning approaches, including clustering algorithms and dimensionality reduction techniques, are enabling researchers to identify coherent microbial community structures within high-dimensional compositional data. More recent deep learning architectures — convolutional and recurrent neural networks — are extracting temporal and spatial patterns in host-microbe interaction data that conventional statistical methods cannot resolve. Notably, the review highlights the emerging role of large language models (LLMs) in functional genomics, where the sequential nature of microbial genetic data shares structural similarities with natural language, allowing transformer-based models to infer functional capacity from raw sequence information. These methods are being applied across a translational spectrum: from disease diagnostics using microbial biomarker signatures, to precision microbiome engineering aimed at therapeutic modulation.
The field faces genuine headwinds that this review candidly acknowledges. Microbiome datasets are notoriously heterogeneous across cohorts, collection protocols, and sequencing platforms, which challenges model generalizability. AI models trained on Western or hospital-based populations may perform poorly across diverse global microbiomes. The causal directionality between microbial signatures and disease states also remains a fundamental interpretive problem that AI pattern recognition cannot resolve alone. That said, the convergence of LLMs with multi-omic microbiome data represents a potentially paradigm-shifting development — one that could accelerate the identification of microbial intervention targets far faster than hypothesis-driven experimentation alone. This is confirmatory of a major trend but also serves as a genuinely useful landmark synthesis for the field.
