Artificial intelligence in colonoscopy: Enhancing quality indicators for optimal patient outcomes

Colonoscopy remains the cornerstone of colorectal cancer prevention and surveillance, but the procedure's effectiveness is entirely dependent upon various quality indicators, such as detection rates, withdrawal time, adequate bowel preparation, cecal intubation rate and patient outcomes. Despite progress in endoscopic techniques, challenges persist in maintaining endoscopists' consistent performance and improving quality metrics. Artificial intelligence (AI) has emerged as a "game changer" in the gastroenterology field, offering the opportunity to significantly increase colonoscopy quality. This review highlights the role of AI-driven technologies such as deep learning, computer vision, and real-time feedback mechanisms in optimizing key quality indicators of colonoscopy. The implementation of AI in colonoscopy may reduce human error, improve endoscopist's consistency in real-time decision making, ensuring higher reliability and standardization during the procedure. Furthermore, AI has the potential to reshape how endoscopists perform and evaluate procedures, while improved lesion characterization may enable more precise selection for resection, reducing morbidity and the incidence of interval cancers. The review also addresses challenges and limitations in AI integration, including cost-effectiveness and its impact on endoscopist training. AI holds substantial promise for advancing colonoscopy quality and elevating overall patient care, paving the way for more effective and personalized medical approaches.