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

Table 1 Summary of representative artificial intelligence studies in irritable bowel syndrome, with evidence grade

Ref.	AI method and task	Population and sample size	Primary outcome definition	Validation approach	Reported performance and uncertainty	Events or responders reported	Evidence grade	Key limitation
Maslekar et al[19], 2010	Artificial neural network; predict organic pathology to prioritise colonoscopy	300 unselected colorectal-clinic attendees; no IBS cohort	Positive finding at lower gastrointestinal endoscopy (polyp, cancer, diverticular disease or colitis)	Internal validation plus external set of 50 patients	95% training; 90% internal validation (95%CI: 84-96) vs 75% clinical assessment; 89% external	Not reported	Indirect	Non-IBS population; predates modern validation standards
Tap et al[21], 2017	Supervised discriminant analysis; microbiota signature of symptom severity	IBS patients and controls; faecal and mucosal microbiota	IBS symptom severity category (severe vs mild-to-moderate)	Internal only	90-OTU signature tracking severity, independent of bowel-habit subtype; no discrimination metric reported	Not reported	Direct	Association only; not a diagnostic model
Vervier et al[28], 2022	Unsupervised metagenomic stratification; subtypes and low-FODMAP response	IBS patients; shotgun metagenomics	Metagenomic subtype assignment (IBSP/IBSH) and symptom response to low-FODMAP diet	Internal only	Greater FODMAP response in IBSP (P = 0.02); no AUC or confidence interval reported	Not reported	Direct	Small cohort; requires external validation
Mars et al[10], 2020	Longitudinal multi-omics integration; subset-specific mechanisms	IBS-C, IBS-D and controls; serial multi-omics	Not applicable; mechanistic analysis with no prediction target	Not applicable	Purine metabolism identified as a host-microbial pathway; no predictive metric	Not applicable	Direct	Mechanistic; no predictive classifier derived
Kraszewski et al[17], 2021	Random forest; classify IBD from routine laboratory markers	702 records (Crohn’s, ulcerative colitis, non-inflammatory controls); no IBS patients	Crohn’s disease or ulcerative colitis vs non-inflammatory control status	Internal discovery cohort only	Mean average precision 97% (Crohn’s), 91% (ulcerative colitis); no confidence intervals reported	702 records from 372 patients (192 Crohn’s disease, 180 ulcerative colitis)	Indirect	No IBS group; not a test of IBS discrimination
Bennet et al[41], 2018	Multivariate model; predict low-FODMAP responsiveness	61 IBS patients analysed; faecal bacterial profiles	Responder defined as a reduction in IBS-SSS of at least 50 points	None	Responders discriminated from non-responders; no sensitivity, specificity or AUC reported	61 of 67 randomised patients analysed; responder counts not reported by arm	Direct	No discrimination metrics; no external validation
Chumpitazi et al[42], 2015	Association analysis; baseline taxa and low-FODMAP response	33 children with IBS; 48-hour crossover diet periods	Responder defined as at least 50% reduction in abdominal pain frequency	None	Responders enriched at baseline for saccharolytic taxa; no discrimination metric reported	33 children; 48-hour diet periods with 5-day washout	Direct (paediatric)	No predictive model; direction of effect discordant with Bennet et al[41]
Su et al[30], 2026	Deep learning; donor-recipient microbiome matching for FMT	515 FMTs, 30 cohorts, 12 indications including IBS	Clinical response following faecal microbiota transplantation	Retrospective simulation across cohorts	AUC 0.88; 78.7% outcome accuracy; simulated response 49%→71%; no confidence intervals reported	515 FMTs across 30 cohorts and 12 indications	Indirect	Not IBS-specific; retrospective and simulated, not prospective
Dowrick et al[14], 2024	Unsupervised machine learning; subtyping disorders of gut-brain interaction	Population symptom data	Not applicable; evaluates stability of unsupervised subtypes	Not applicable	Demonstrates instability of data-driven subtyping	Not applicable	Direct (DGBI)	Cautionary; subgroups sensitive to methodological choice
Bhagavathula et al[18], 2025	Systematic review; diagnostic accuracy of AI in IBS	6 studies; 1366 participants	Diagnostic accuracy of AI models against the reference IBS diagnosis	Not applicable (systematic review)	Accuracy 54%-98%; AUC 0.61-0.99 across studies; meta-analysis not feasible	6 studies; 1366 participants	Direct (review)	Few studies; variable biomarkers and validation

AI: Artificial intelligence; IBS: Irritable bowel syndrome; OTU: Operational taxonomic unit; AUC: Area under the curve; IBS-C: Irritable bowel syndrome with predominant constipation; IBS-D: Irritable bowel syndrome with predominant diarrhea; FMT: Faecal microbiota transplantation; DGBI: Disorders of gut-brain interaction; FODMAP: Fermentable oligosaccharides, disaccharides, monosaccharides and polyols; IBS^P: Irritable Bowel Syndrome pathogenic-like; IBS^H: Irritable Bowel Syndrome health-like; IBS-SSS: Irritable bowel syndrome severity scoring system.