Artificial intelligence-assisted colonoscopy: Transforming detection, characterization, and management of colorectal neoplasia

Vollständiger Abstract

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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.