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Wang HR, Wang HL. Artificial intelligence in functional gastrointestinal disorders: From symptom recognition to personalised management. Artif Intell Gastroenterol 2026; 7(3): 127380 [DOI: 10.35712/aig.127380]
Artif Intell Gastroenterol. Nov 8, 2026; 7(3): 127380 Published online Nov 8, 2026. doi: 10.35712/aig.127380
Artificial intelligence in functional gastrointestinal disorders: From symptom recognition to personalised management
Han-Run Wang, He-Lei Wang
Han-Run Wang, Department of Clinical Medicine, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China
He-Lei Wang, Department of Gastrocolorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China
Author contributions: Wang HR performed the literature review, drafted the manuscript, and prepared Figure 1; Wang HL conceived and designed the review, critically revised the manuscript for important intellectual content, supervised the study, and approved the final manuscript. All authors have read and approved the final version of the manuscript.
AI contribution statement: ChatGPT was only used for language polishing.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: He-Lei Wang, PhD, Additional Professor, Associate Chief Physician, Department of Gastrocolorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Xinmin Street, Changchun 130021, Jilin Province, China. helei@jlu.edu.cn
Received: August 28, 2026 Revised: September 22, 2026 Accepted: October 13, 2026 Published online: November 8, 2026 Processing time: 67 Days and 18.2 Hours
Abstract
Functional gastrointestinal disorders, now termed disorders of gut-brain interaction, are heterogeneous conditions shaped by psychological, physiological, microbial, and environmental factors. Diagnosis and treatment remain difficult because symptoms overlap, trajectories fluctuate, and no single objective biomarker exists. This minireview synthesises human evidence for artificial intelligence (AI) in symptom classification, digital phenotyping, microbiome and multi-omics prediction, treatment-response estimation, wearable monitoring, and language-based care. Targeted PubMed searches were updated through September 20, 2026. Current studies show feasibility, but most use small, single-centre cohorts, retrospective designs, or internal validation; calibration and prospective impact evaluation are uncommon. The most plausible near-term role of AI is therefore clinical augmentation: Identifying longitudinal patterns, supporting shared decisions, and detecting changes that require reassessment while preserving human oversight and patient autonomy. Large language models may improve education and communication, but disorder-specific evaluations reveal imperfect diagnosis and treatment recommendations with marked overconfidence. Translation requires representative cohorts that include overlapping organic disease, explicit endpoints despite the absence of a diagnostic gold standard, external validation, calibration, equity assessment, and prospective evaluation of patient benefit. AI should be judged by outcomes and care experience, not discrimination alone.
Core Tip: This minireview examines the clinical and translational use of artificial intelligence in adults and children with disorders of gut-brain interaction, especially irritable bowel syndrome and functional dyspepsia. It covers machine-learning phenotyping, microbiome and multi-omics prediction, treatment-response estimation, digital monitoring, wearable signals, natural language processing, and large language models. Evidence is interpreted according to study design, sample size, algorithm, validation, calibration, and clinical endpoint; exploratory performance is not treated as proof of clinical benefit.
