Recent advances in the integration of artificial intelligence (AI) and microbiome analysis have expanded our understanding of gastrointestinal diseases, particularly in inflammatory bowel disease (IBD), colitis-associated colorectal cancer (CAC), and sporadic colorectal cancer (CRC). While IBD and CAC are mechanistically linked, recent evidence also implicates dysbiosis in sporadic CRC. The progression from IBD to CAC is mechanistically linked through chronic inflammation and microbial dysbiosis, whereas distinct dysbiotic patterns are also observed in sporadic CRC. In this review, we examined how machine learning (ML) and AI were applied to the microbiome and multi-omics data, which enabled the discovery of non-invasive microbial biomarkers, refined risk stratification, and prediction of treatment response. We highlighted how emerging computational frameworks, including explainable AI (xAI), graph-based models, and integrative multi-omics, were advancing the field from descriptive profiling toward predictive and prescriptive analytics. While emphasizing these innovations, we also critically assessed current limitations, including data variability, the lack of methodological standardization, and challenges in clinical translation. Collectively, these developments enabled AI-powered microbiome research as a driving force for precision medicine in IBD, CAC, and sporadic CRC.

Recent advances in the integration of artificial intelligence (AI) and microbiome analysis have expanded our understanding of gastrointestinal diseases, particularly in inflammatory bowel disease (IBD), colitis-associated colorectal cancer (CAC), and sporadic colorectal cancer (CRC). While IBD and CAC are mechanistically linked, recent evidence also implicates dysbiosis in sporadic CRC. The progression from IBD to CAC is mechanistically linked through chronic inflammation and microbial dysbiosis, whereas distinct dysbiotic patterns are also observed in sporadic CRC. In this review, we examine how machine learning (ML) and AI are applied to the microbiome and multi‑omics data which enables the discovery of non‑invasive microbial biomarkers, refined risk stratification, and prediction of treatment response. We highlight how emerging computational frameworks, including explainable AI (xAI), graph‑based models, and integrative multi‑omics, are advancing the field from descriptive profiling toward predictive and prescriptive analytics. While emphasizing these innovations, we also critically assess current limitations, including data variability, lack of methodological standardization, and challenges in clinical translation. Collectively, these developments enable AI‑powered microbiome research as a driving force for precision medicine in IBD, CAC, and sporadic CRC.

Malignant tumors require substantial energy sources for proliferation, and dietary composition may influence colorectal carcinogenesis through metabolic and microbiome-related mechanisms. This study investigated the association of low-carbohydrate high-fat dietary patterns with macroscopic tumor burden, morphologic inflammatory cell infiltration, and gut microbiome alterations using an azoxymethane/dextran sulfate sodium (AOM/DSS)-induced mouse model of colitis-associated colorectal cancer. Male C57BL/6 mice received AOM followed by three cycles of DSS and were fed a standard diet (SD), high-carbohydrate diet (HCD), low-carbohydrate high-fat lard-based diet (HFL), or low-carbohydrate high-fat coconut oil-based diet (HFC). Body weight, colon length, splenic weight, macroscopic tumor formation, hematoxylin and eosin (H&E)-based inflammatory cell infiltration, and gut microbiome composition were analyzed. The HFL and HFC groups exhibited higher body weights and relatively preserved colon lengths compared with the SD and HCD groups. Tumor number and total tumor size were reduced in the HFL and HFC groups. Total lymphocyte-like inflammatory cell infiltration was not increased in the high-fat diet groups, whereas per-tumor values were interpreted cautiously because they are affected by tumor number. Gut microbiome analysis demonstrated altered microbial composition, increased alpha diversity, and distinct temporal microbial dynamics in the high-fat diet groups. Because the HFL and HFC diets simultaneously changed carbohydrate content, fat content, fat source, and caloric density, these findings should be interpreted as exploratory effects of low-carbohydrate high-fat dietary patterns rather than independent effects of carbohydrate restriction, total fat, or fat source.