Aliaga Ramos J. Artificial intelligence-assisted colonoscopy: Transforming detection, characterization, and management of colorectal neoplasia. Artif Intell Gastrointest Endosc 2026; 7(2): 121689 [DOI: 10.37126/aige.121689]
Corresponding Author of This Article
Josué Aliaga Ramos, MD, Doctor, Service of Gastroenterology, Hospital José Agurto Tello-Chosica, 229 Walter Stubbs Street, Lima 150118, Peru. arjosue3000@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
review-article
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artificial intelligence-assisted colonoscopy: Transforming detection, characterization, and management of colorectal neoplasia
Josué Aliaga Ramos
Josué Aliaga Ramos, Service of Gastroenterology, Hospital José Agurto Tello-Chosica, Lima 150118, Peru
Author contributions: Aliaga Ramos J conceived and designed the study, performed the literature review, conducted the data interpretation, wrote the original draft, revised the manuscript critically for important intellectual content, and approved the final version of the manuscript.
AI contribution statement: No artificial intelligence (AI) tools were used in the preparation of this manuscript. The study conception, literature review, critical analysis, writing, revision, and final approval were performed entirely by the author.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Corresponding author: Josué Aliaga Ramos, MD, Doctor, Service of Gastroenterology, Hospital José Agurto Tello-Chosica, 229 Walter Stubbs Street, Lima 150118, Peru. arjosue3000@gmail.com
Received: March 31, 2026 Revised: July 13, 2026 Accepted: August 3, 2026 Published online: September 8, 2026 Processing time: 158 Days and 1.6 Hours
Abstract
Colorectal cancer remains one of the leading causes of cancer-related morbidity and mortality worldwide, and its prevention largely depends on the accurate detection and characterization of precursor lesions during colonoscopy. This narrative review provides an updated overview of the current role of artificial intelligence (AI) in colorectal neoplasia based on a structured literature search of the PubMed/MEDLINE database, focusing primarily on studies published between 2018 and 2025, including randomized controlled trials, prospective studies, systematic reviews, meta-analyses, and recent clinical practice guidelines. AI-assisted colonoscopy, particularly through computer-aided detection and computer-aided diagnosis (CADx) systems, has consistently demonstrated improvements in colonoscopic performance. Randomized controlled trials and meta-analyses have shown absolute increases in adenoma detection rate of approximately 10%-15%, accompanied by reductions in adenoma miss rates. In parallel, CADx systems have achieved diagnostic accuracies frequently exceeding 90% for the optical characterization of diminutive colorectal polyps, supporting real-time strategies such as resect and discard and, in selected cases, diagnose and leave. Emerging evidence also suggests potential applications in colorectal cancer risk stratification, therapeutic decision-making, and precision endoscopy. Despite these advances, important challenges remain, including false-positive detections, limited external validation, algorithm generalizability across diverse populations and endoscopic platforms, implementation costs, interoperability, and unresolved regulatory and medico-legal issues. Recent international guidelines recognize AI as a valuable adjunct that improves lesion detection and supports optical diagnosis, while emphasizing the need for additional evidence demonstrating long-term clinical benefits, including reductions in post-colonoscopy colorectal cancer incidence and mortality. Current evidence supports AI as an effective decision-support tool that complements rather than replaces the expertise and clinical judgment of the endoscopist. Continued multicenter validation, real-world implementation studies, and prospective evaluation of patient-centered outcomes will be essential to define its optimal role in routine colorectal cancer prevention and therapeutic endoscopy.
Core Tip: Artificial intelligence (AI) is rapidly redefining the landscape of colorectal neoplasia, emerging as a pivotal driver of a new era in precision endoscopy. By augmenting human perception with advanced pattern recognition and real-time data processing, AI-enabled systems have demonstrated a consistent capacity to enhance adenoma detection, refine optical diagnosis, and support therapeutic decision-making with unprecedented accuracy and reproducibility. Beyond improving key quality indicators such as adenoma detection rate and diagnostic performance, AI mitigates operator-dependent variability and establishes a more standardized and objective approach to endoscopic practice. Its integration across the entire continuum of care from risk stratification and screening to lesion characterization and advanced endoscopic resection positions AI as a transformative tool capable of optimizing colorectal cancer prevention strategies. As evidence continues to accumulate, AI is no longer a complementary technology but a central component in the evolution toward more effective, individualized, and outcome-driven management of colorectal neoplasia.
