Yan YN, Zeng JQ, Ding X. Artificial intelligence in functional gastrointestinal disorders: From precision diagnosis to preventive healthcare. Artif Intell Gastroenterol 2026; 7(1): 112357 [DOI: 10.35712/aig.v7.i1.112357]
Corresponding Author of This Article
Jing-Qi Zeng, PhD, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Yangguang South Street, Fangshan District, Beijing 102488, China. zjingqi@163.com
Research Domain of This Article
Computer Science, Artificial Intelligence
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artif Intell Gastroenterol. Jan 8, 2026; 7(1): 112357 Published online Jan 8, 2026. doi: 10.35712/aig.v7.i1.112357
Artificial intelligence in functional gastrointestinal disorders: From precision diagnosis to preventive healthcare
Yi-Nan Yan, Jing-Qi Zeng, Xia Ding
Yi-Nan Yan, Xia Ding, School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
Jing-Qi Zeng, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
Co-corresponding authors: Jing-Qi Zeng and Xia Ding.
Author contributions: Zeng JQ conceptualized the study, supervised the project, and critically revised the manuscript; Yan YN contributed to the literature review, data collection, and drafting of the manuscript; Ding X provided guidance on study design, critical revisions, and final approval of the manuscript.
Supported by The Natural Science Foundation of China, No. 82374292; the Plans for Major Provincial Science and Technology Projects of Anhui Province, No. 202303a07020003; and the Innovation Team and Talents Cultivation Program of the National Administration of Traditional Chinese Medicine, No. ZYYCXTD-C-202401.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jing-Qi Zeng, PhD, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Yangguang South Street, Fangshan District, Beijing 102488, China. zjingqi@163.com
Received: July 24, 2025 Revised: August 27, 2025 Accepted: January 4, 2026 Published online: January 8, 2026 Processing time: 166 Days and 1.6 Hours
Abstract
Functional gastrointestinal disorders (FGIDs), including irritable bowel syndrome (IBS), functional dyspepsia (FD), and gastroesophageal reflux disease (GERD), present persistent diagnostic and therapeutic challenges due to symptom heterogeneity and the absence of reliable biomarkers. Artificial intelligence (AI) enables the integration of multimodal data to enhance FGID management through precision diagnostics and preventive healthcare. This minireview summarizes recent advancements in AI applications for FGIDs, highlighting progress in diagnostic accuracy, subtype classification, personalized interventions, and preventive strategies inspired by the traditional Chinese medicine concept of “treating the undiseased”. Machine learning and deep learning algorithms have demonstrated value in improving IBS diagnosis, refining FD neuro-gastrointestinal subtyping, and screening for GERD-related complications. Moreover, AI supports dietary, psychological, and integrative medicine-based interventions to improve patient adherence and quality of life. Nonetheless, key challenges remain, including data heterogeneity, limited model interpretability, and the need for robust clinical validation. Future directions emphasize interdisciplinary collaboration, the development of multimodal and explainable AI models, and the creation of patient-centered platforms to facilitate a shift from reactive treatment to proactive prevention. This review provides a systematic framework to guide the clinical application and theoretical innovation of AI in FGIDs.
Core Tip: Artificial intelligence (AI) is reshaping the management of functional gastrointestinal disorders by integrating clinical, physiological, and imaging data for precise diagnosis, refined subtyping, and tailored treatments. Preventive approaches, including those informed by traditional Chinese medicine, show promise in improving patient outcomes. However, data variability and limited model transparency remain key challenges. Advances in interpretable and clinically validated AI will support a shift from reactive treatment to proactive and preventive care.
