Translating artificial intelligence into clinical practice for gastrointestinal endoscopy: current applications and...

Artificial intelligence (AI) has emerged as a transformative tool in gastrointestinal (GI) endoscopy, addressing challenges in detection, diagnosis, and decision-making. In upper GI endoscopy, AI supports blind spot monitoring, Helicobacter pylori diagnosis, and the identification of premalignant and malignant lesions, with high accuracy and reduced miss rates. In lower GI endoscopy, computer-aided detection improves adenoma detection, whereas computer-aided diagnosis supports "resect-and-discard" and "diagnose-and-leave" strategies. However, real-world benefits remain modest, with concerns regarding overdetection and variable performance across lesion types and colon segments. In inflammatory bowel disease, AI standardizes endoscopic and histologic scoring, reduces interobserver variability, and accelerates capsule endoscopy interpretation, including high diagnostic accuracy for Crohn's disease. Pancreatobiliary applications, including endoscopic ultrasound, endoscopic retrograde cholangiopancreatography, and cholangioscopy, demonstrate strong performance in differentiating pancreatic masses and biliary strictures and in predicting postprocedural complications. Despite expert-level performance across multiple domains, most studies remain single-center or retrospective, and explainability, workflow integration, medicolegal responsibility, and cost-effectiveness continue to limit adoption. Emerging solutions, including explainable AI and AI-generated common data model-compatible reports, may bridge these gaps. With rigorous multicenter validation and real-world implementation, AI can evolve from an experimental adjunct into a core component of routine endoscopic practice.