Role of artificial intelligence in irritable bowel syndrome: A narrative review

Irritable bowel syndrome (IBS) is a prevalent yet incompletely understood functional gastrointestinal disorder whose estimated prevalence depends heavily on the diagnostic criteria applied, falling from approximately 9% under Rome III to approximately 4% under Rome IV.Characterized by recurrent abdominal pain and altered bowel habits, it substantially impairs quality of life and imposes a considerable economic burden.Its pathophysiology is complex and multifactorial, involving visceral hypersensitivity, altered gastrointestinal motility, immune activation, gut-brain axis dysregulation, and intestinal dysbiosis.This narrative review examines the potential of artificial intelligence (AI) to refine the diagnosis, subclassification, and management of IBS and related functional gastrointestinal disorders.We discuss the principal AI methodologies -machine learning, deep learning, and natural language processing -and how they enable the integration of diverse data sources, including patient-reported outcomes, microbiome profiles, and multi-omics data.Early studies suggest AI can support the differentiation of IBS from organic disease, the identification of biologically distinct microbiota subgroups, and the prediction of response to the low-fermentable oligosaccharides, disaccharides, monosaccharides and polyols diet.These findings, however, derive largely from small, single-center cohorts with heterogeneous methods and without external validation; a 2025 systematic review identified only six eligible studies comprising 1366 participants and found the evidence too heterogeneous to permit meta-analysis.We further identify domains in which AI is frequently invokedbut not yet evidenced in IBS, including machine-learning analysis of longitudinal patient-collected symptom data and of psychological comorbidity.Substantial challenges remain around data standardization, external and temporal validation, model interpretability, and ethical and regulatory considerations.Progress will depend less on the development of further models than on standardized data collection, multicenter validation, and reporting against AI-specific standards such as transparent 3 / 49 reporting of a multivariable prediction model for individual prognosis or diagnosis + AI.