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

Abstract

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 invoked but 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, multi-center validation, and reporting against AI-specific standards such as transparent reporting of a multivariable prediction model for individual prognosis or diagnosis + AI.

Keywords: Artificial intelligence; Machine learning; Irritable bowel syndrome; Disorders of gut-brain interaction; Gut microbiome; Natural language processing; Digital health; Personalized medicine

Core Tip: Enthusiasm for artificial intelligence in irritable bowel syndrome has outpaced its evidence base. Across diagnosis, microbiota subtyping and treatment prediction, the supporting studies are small, single-center and largely externally unvalidated; a 2025 systematic review found only six eligible studies and could not pool them. Several widely repeated capabilities - machine-learning analysis of longitudinal symptom data, and of psychological comorbidity - have no peer-reviewed IBS evidence that we were able to identify. The field’s priority should therefore shift from developing further models toward standardized data collection, multi-center external validation, and reporting against artificial intelligence-specific standards.