Artificial Intelligence Applied to the Brain-Gut Axis in Irritable Bowel Syndrome: Advancing Toward Clinical Translation

Irritable bowel syndrome (IBS) is one of the most common functional gut disorders affecting the global population, characterized by chronic abdominal pain and altered bowel habits in the absence of structural disease. The brain-gut-microbiota axis, a bidirectional network integrating central nervous system processing, enteric and autonomic function, immune signaling, and gut microbial ecology, provides a mechanistic framework that helps explain the substantial symptom heterogeneity and variable treatment response observed across patients. Artificial intelligence (AI) and machine learning (ML) approaches offer the ability to model complex, nonlinear relationships across high-dimensional biological datasets generated from this axis, including microbiome composition profiles, resting-state functional MRI connectivity matrices, multiomics data layers, and psychological and clinical feature sets. This narrative review evaluated primary human studies applying AI and ML to brain-gut axis data in IBS, identified through structured searches of PubMed/MEDLINE and Scopus supplemented by citation chaining, with literature included up to April 2026. Across microbiome profiling, neuroimaging, multiomics integration, and psychological feature modeling, ML approaches have demonstrated proof-of-concept performance for IBS classification and, in a smaller number of studies, for prediction of clinically meaningful outcomes, including cognitive behavioral therapy (CBT) response. A notable early signal is the integration of baseline microbiome and brain features to predict CBT response, with high reported discrimination, although these results are derived from small, single-center cohorts with only internal validation and should be regarded as hypothesis-generating. The current evidence base is limited by small single-center cohorts, reliance on internal validation, healthy-control comparators, limited external replication, and substantial overfitting and data-leakage risk in high-dimensional small-sample settings. AI and ML applications in IBS are promising but remain exploratory and are not yet suitable for routine clinical use. Clinical translation will require larger multicenter datasets, harmonized preprocessing pipelines, external validation, calibration reporting, and evaluation against clinically realistic comparators and decision points.