Artificial intelligence in gastroenterology practice: Current perspectives and emerging prospects

Artificial intelligence (AI) is increasingly changing the landscape of gastroenterology by improving disease detection, diagnosis, risk stratification, prognostication, and clinical decision-making.Machine learning, deep learning, natural language processing, large language models, and multimodal data integration have facilitated the development of accurate diagnostic and predictive algorithms for a wide spectrum of gastrointestinal and hepatopancreatobiliary diseases.In upper gastrointestinal diseases, AI has enhanced the detection and characterization of gastroesophageal reflux disease, Barrett's esophagus, esophageal cancer, gastric neoplasia, and gastroesophageal varices.The use of AI in capsule endoscopy has facilitated small intestinal mucosal lesion detection and reduced reading time.Computer-aided detection and diagnosis systems have improved adenoma detection and polyp characterization in colorectal diseases, while AI is increasingly being applied to molecular profiling and prognostic modeling in colorectal cancer.In inflammatory bowel disease, AI facilitates more objective endoscopic and histologic assessment, as well as prediction of disease outcome and treatment response.Promising AI-based radiomics, digital pathology, biomarker discovery, and risk prediction models have been emerging in pancreatic, biliary, and liver diseases.Large language models and multimodal AI may further enable integration of heterogeneous information and support personalized decision-making.Computer-aided quality assessment represents an emerging application for standardizing procedural quality in endoscopy.Several AIassisted platforms have received regulatory approval with ongoing integration into clinical practice.However, limited external validation, population and device-related heterogeneity, dataset bias, interpretability, workflow integration, and uncertain costeffectiveness remain important barriers to generalizability and clinical adoption.Robust multicenter prospective validation and responsible implementation will be essential to optimize its clinical impact. / 85