Attieh P, Al Hazzouri A, Moubayed R, Youssef T, Karam K, Farhat SG. Advances in artificial intelligence-based colonoscopic tools and modalities: Transforming colorectal cancer detection and management. Artif Intell Gastroenterol 2026; 7(2): 120803 [DOI: 10.35712/aig.120803]
Corresponding Author of This Article
Said G Farhat, Associate Professor, Department of Internal Medicine, Division of Gastroenterology, Saint Georges Hospital University Medical Center, Rmeil Street, Ashrafieh, Beirut 3187, Beyrouth, Lebanon. saidfarhat@hotmail.com
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Gastroenterology & Hepatology
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review-article
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Attieh P, Al Hazzouri A, Moubayed R, Youssef T, Karam K, Farhat SG. Advances in artificial intelligence-based colonoscopic tools and modalities: Transforming colorectal cancer detection and management. Artif Intell Gastroenterol 2026; 7(2): 120803 [DOI: 10.35712/aig.120803]
Artif Intell Gastroenterol. Aug 8, 2026; 7(2): 120803 Published online Aug 8, 2026. doi: 10.35712/aig.120803
Advances in artificial intelligence-based colonoscopic tools and modalities: Transforming colorectal cancer detection and management
Philippe Attieh, Antonio Al Hazzouri, Rim Moubayed, Tya Youssef, Karam Karam, Said G Farhat
Philippe Attieh, Department of General Surgery, University of Balamand, Beirut 1100, Beyrouth, Lebanon
Antonio Al Hazzouri, Rim Moubayed, Tya Youssef, Department of Internal Medicine, University of Balamand, Beirut 1003, Beyrouth, Lebanon
Karam Karam, Department of Gastroenterology, University of Balamand, Beirut 1003, Beyrouth, Lebanon
Said G Farhat, Department of Internal Medicine, Division of Gastroenterology, Saint Georges Hospital University Medical Center, Beirut 3187, Beyrouth, Lebanon
Author contributions: Farhat SG contributed to the conceptualization; Attieh P, Al Hazzouri A, Moubayed R, Youssef T, Karam K, and Farhat SG contributed to writing the original draft preparation, review and editing; Farhat SG contributed to supervision and project administration; and all authors have read and agreed to the published version of the manuscript.
AI contribution statement: We confirm that no artificial intelligence technology was used in the process of writing this manuscript.
Conflict-of-interest statement: All authors declare that they have no conflict of interest to disclose.
Corresponding author: Said G Farhat, Associate Professor, Department of Internal Medicine, Division of Gastroenterology, Saint Georges Hospital University Medical Center, Rmeil Street, Ashrafieh, Beirut 3187, Beyrouth, Lebanon. saidfarhat@hotmail.com
Received: March 9, 2026 Revised: March 29, 2026 Accepted: May 20, 2026 Published online: August 8, 2026 Processing time: 151 Days and 13.7 Hours
Abstract
Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide, and colonoscopy is the gold standard for screening and polyp detection. However, its effectiveness is operator-dependent, with variability in adenoma detection rates (ADR). Artificial intelligence (AI), particularly deep learning-based systems, has emerged as a promising tool to enhance colonoscopic performance. This narrative review was conducted through a structured literature search of PubMed, Scopus, and Web of Science databases for studies published up to 2025. Eligible studies included randomized controlled trials, systematic reviews, and meta-analyses evaluating AI-assisted colonoscopy, with a focus on computer-aided detection (CADe) and computer-aided diagnosis (CADx). Evidence consistently demonstrates that CADe systems improve ADR and polyp detection rate, primarily by enhancing detection of diminutive and flat lesions and reducing adenoma miss rates. CADx systems enable real-time optical diagnosis, supporting “resect-and-discard” and “diagnose-and-leave” strategies, although their diagnostic accuracy and generalizability remain variable. Beyond detection and characterization, AI applications extend to quality assurance metrics, including bowel preparation assessment and withdrawal time monitoring. Despite promising results, challenges remain, including heterogeneity among AI systems, cost-effectiveness, regulatory considerations, and integration into clinical workflows. Emerging multimodal AI approaches combining endoscopic, clinical, and molecular data may further improve risk stratification and personalized surveillance. This review provides a comprehensive synthesis of current evidence, offers critical appraisal of existing studies, and highlights future directions aimed at achieving precision-driven CRC screening and improved clinical outcomes.
Core Tip: Artificial intelligence (AI)-assisted colonoscopy has emerged as a promising tool for improving the quality of colorectal cancer (CRC) screening. Evidence from randomized controlled trials and meta-analyses consistently shows that computer-aided detection systems significantly increase adenoma detection rate and polyp detection rate, particularly for small and flat lesions that are commonly missed during conventional colonoscopy. However, improvements in detecting advanced adenomas and CRC remain limited. Additionally, AI may increase the removal of non-neoplastic polyps. Overall, AI technologies serve as valuable decision-support tools that enhance lesion detection, reduce operator variability, and may contribute to more effective CRC prevention strategies in clinical practice.
