Best of artificial intelligence in GI endoscopy (Gastrointestinal Endoscopy)

AI in GI endoscopy has moved from “cool demos” to clinically tested tools, with the strongest evidence in colonoscopy. Across multiple randomized trials and meta-analyses, computer-aided detection (CADe) consistently boosts polyp/adenoma detection (about a ~20% lift on average), while newer systems are reducing the false-alert problem that can drive “alert fatigue.” Computer-aided diagnosis (CADx) is improving real-time polyp characterization, but still needs stronger prospective data before “resect-and-discard” becomes routine. Beyond detection, quality-control AI (cecal intubation confirmation, bowel prep scoring, withdrawal optimization) is emerging as the next leap—shifting toward full “AI-guided endoscopy platforms.” Upper GI, EUS/ERCP, capsule, and IBD surveillance applications look promising (high reported accuracies, big time savings), but broad adoption hinges on workflow integration, diverse data, interpretability, and patient-outcome trials.