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.

Background: Postoperative liver regeneration is essential for maintaining hepatic function. This study evaluated the rate, determinants, and volumetric patterns of early liver regeneration after hemihepatectomy.Methods: A retrospective review was conducted of 50 patients who underwent right or left hemihepatectomy between April 2019 and March 2025. Liver and spleen volumes (SV) were assessed preoperatively, at postoperative day (POD) 1 week, and at POD 3 months. Early liver regeneration rate (LRR) was defined as the percentage increase in remnant liver volume at POD 1 week relative to the preoperative future liver remnant (FLR), and patients were categorized into low (<50%) and high (≥50%) LRR groups. Clinical, biochemical, and volumetric variables were compared, and predictors of regeneration were identified using multivariable analyses. Regeneration patterns were also examined according to whether the FLR/standard liver volume (SLV) ratio was <50% or ≥50%.Results: FLR/SLV was the strongest independent predictor of rapid early liver regeneration (p<0.001). Remnants with FLR/SLV <50% exhibited rapid and sustained regeneration, whereas those with FLR/SLV ≥50% showed slower regrowth that plateaued after reaching approximately 90% of SLV. SV increased at POD 1 week in all patients; however, only the FLR/SLV ≥50% group showed a reduction by POD 3 months, whereas the <50% group maintained elevated volumes.Conclusions: FLR/SLV reliably predicts early postoperative liver regeneration. Smaller remnants regenerate more rapidly, whereas persistent splenic enlargement suggests a sustained portal hemodynamic burden. Combined evaluation of FLR/SLV and SV may enhance perioperative risk assessment and surgical planning.

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.