Artificial intelligence in functional gastrointestinal disorders: From precision diagnosis to preventive healthcare

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.