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

Table 2 Artificial intelligence methods and their current status in irritable bowel syndrome

Method family	Core principle	Typical application	Current status in IBS
Supervised learning (random forest, support vector machine, gradient boosting)	Learns a mapping from labelled examples to a defined outcome	Diagnostic classification; treatment-response prediction	The most common approach; small single-centre cohorts, little external validation
Unsupervised learning (clustering, latent class and mixture models)	Identifies structure without predefined labels	Subtype and endotype discovery	Applied to microbiota and symptom subgrouping; subgroup stability sensitive to method
Deep learning - convolutional neural networks	Hierarchical feature learning from images	Endoscopic image analysis; optical histology prediction	Established in luminal endoscopy; minimal role in functional disease
Deep learning - recurrent networks and transformers	Models sequential or multi-modal data	Longitudinal risk prediction from clinical records	Demonstrated in general clinical records; no published IBS application
Natural language processing and large language models	Extracts structured information from free text	Symptom and outcome extraction from notes and narratives	Demonstrated in inflammatory bowel disease; not yet reported in IBS
Reinforcement learning	Optimises sequential decisions from outcome feedback	Adaptive treatment protocols	Conceptual only in IBS; demonstrated in critical care

IBS: Irritable bowel syndrome.