Artificial intelligence in functional gastrointestinal disorders: From symptom recognition to personalised management

Table 1 Representative artificial intelligence studies in disorders of gut-brain interaction

Ref. and task	Data and sample	Algorithm	Reported performance	Validation	Main limitations
Andriulli et al[20], 2003: Dyspepsia classification	Multicentre observational cohort; n = 860	Artificial neural networks	Sensitivity 83.4% for functional dyspepsia; specificity 87.6% for organic disease	Internal dataset evaluation	Pre-Rome IV labels; no calibration, external validation, or impact study
Mousavi et al[18], 2023: DGBI phenotyping	Population-based cross-sectional cohort; n = 3765	Mixed-data spectral clustering	Eight overlapping clusters after re-clustering	Internal cluster indices	Rome III; cross-sectional; no treatment or external validation
Katsumata et al[21], 2023: Diagnostic support	Brain activity and food preference; n = 41, including 25 DGBI	Artificial neural network	Accuracy 72.3% for DGBI and 77.1% for functional dyspepsia	Internal only	Very small sample; control comparison; no calibration
Zhang et al[27], 2025: IBS classification	Resting-state functional magnetic resonance imaging; 31 IBS and 30 controls	Support vector machine	Accuracy 91.9%	Internal validation	Single centre; small sample; no external calibration
Fukui et al[36], 2020: Microbiome classification	16S sequencing; 85 IBS and 26 controls	Feature selection plus machine learning	Sensitivity > 80%; specificity > 90%	Internal validation	Small, region-specific cohort; no independent validation
Yin et al[42], 2022: Acupuncture response	Two clinical trials; total n = 745; external cohort n = 193	Support vector classification and regression	Internal accuracy 0.773; R2: 0.446 for quality of life and 0.413 for symptoms	Independent cohort from a second trial	Predicts prognosis under one treatment, not comparative treatment benefit
Jacobs et al[41], 2021: Cognitive behavioural therapy response	Imaging n = 84; microbiome n = 34	Random forest with 11 genera	Internal area under the receiver operating characteristic curve: 0.96	Internal only	Very small microbiome sample; high optimism risk; no calibration
Tunali et al[53], 2024: Personalised diet	Multicentre randomised trial; n = 121	Microbiome-based diet algorithm	IBS severity change -112.7 vs -99.9; between-group P = 0.29	Prospective randomised comparison	No superiority for the primary outcome; algorithm transparency limited
Ahn and Parker[45], 2026: DGBI language-model benchmark	68 Rome IV clinical scenarios; five models	General-purpose large language models	Diagnostic accuracy 65%-74%; treatment accuracy 41%-54%; confidence 93%-96%	Scenario benchmark only	Convenience sample; no patient outcomes; marked overconfidence

DGBI: Disorders of gut-brain interaction; IBS: Irritable bowel syndrome.