Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
Artif Intell Gastroenterol. Nov 8, 2026; 7(3): 127380
Published online Nov 8, 2026. doi: 10.35712/aig.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, Department of Clinical Medicine, The First Hospital of Jilin University, Chang
He-Lei Wang, Department of Gastrocolorectal Surgery, General Surgery Center, The First Hos
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
Revised: September 22, 2026
Accepted: October 13, 2026
Published online: November 8, 2026
Processing time: 67 Days and 18.2 Hours
Core Tip
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.