A critical review of technical progress, clinical heterogeneity, and implementation challenges of artificial intelligence in digestive endoscopy

AI demonstrates significant potential to improve endoscopic outcomes by augmenting lesion detection rates and diagnostic precision. However, the translation of AI innovations into routine clinical practice is tempered by challenges, such as variability in clinical effectiveness, dependency on procedural quality, domain generalizability, and cost-effectiveness considerations. Future advancements should focus on enhancing AI robustness, integrating multimodal data, and establishing sustainable implementation frameworks to maximize clinical benefit while maintaining patient safety and ethical standards.