Curlej P, Soldera J. Artificial intelligence in predicting colorectal polyp histology: Systematic review and meta-analysis of diagnostic accuracy in real-time procedures. World J Gastrointest Endosc 2026; 18(3): 116381 [DOI: 10.4253/wjge.v18.i3.116381]
Corresponding Author of This Article
Jonathan Soldera, MD, PhD, Tutor, Department of Gastroenterology and Acute Medicine, University of South Wales in Association with Learna Ltd., 86-88 Adam Street, Cardiff CF37 1DL, United Kingdom. jonathansoldera@gmail.com
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Gastroenterology & Hepatology
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Meta-Analysis
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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/
Mar 16, 2026 (publication date) through Mar 17, 2026
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Publication Name
World Journal of Gastrointestinal Endoscopy
ISSN
1948-5190
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Curlej P, Soldera J. Artificial intelligence in predicting colorectal polyp histology: Systematic review and meta-analysis of diagnostic accuracy in real-time procedures. World J Gastrointest Endosc 2026; 18(3): 116381 [DOI: 10.4253/wjge.v18.i3.116381]
World J Gastrointest Endosc. Mar 16, 2026; 18(3): 116381 Published online Mar 16, 2026. doi: 10.4253/wjge.v18.i3.116381
Artificial intelligence in predicting colorectal polyp histology: Systematic review and meta-analysis of diagnostic accuracy in real-time procedures
Princess Curlej, Jonathan Soldera
Princess Curlej, Department of Gastroenterology, University of South Wales in Association with Learna Ltd., Cardiff CF37 1DL, United Kingdom
Jonathan Soldera, Department of Gastroenterology and Acute Medicine, University of South Wales in Association with Learna Ltd., Cardiff CF37 1DL, United Kingdom
Jonathan Soldera, Department of Gastroenterology, Logan Hospital, Brisbane 4131, Queensland, Australia
Co-first authors: Princess Curlej and Jonathan Soldera.
Author contributions: Curlej P and Soldera J participated in the concept and design research, drafted the manuscript, contributed to data acquisition, analysis and interpretation, and they contributed equally to this manuscript and are co-first authors; Soldera J contributed to study supervision. All authors contributed to critical revision of the manuscript for important intellectual content.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Corresponding author: Jonathan Soldera, MD, PhD, Tutor, Department of Gastroenterology and Acute Medicine, University of South Wales in Association with Learna Ltd., 86-88 Adam Street, Cardiff CF37 1DL, United Kingdom. jonathansoldera@gmail.com
Received: November 11, 2025 Revised: December 10, 2025 Accepted: January 20, 2026 Published online: March 16, 2026 Processing time: 123 Days and 4.9 Hours
Abstract
BACKGROUND
Colorectal cancer remains a major global health burden. Accurate real-time characterization of colorectal polyp histology during colonoscopy is pivotal for early detection and management. Artificial intelligence (AI)-assisted endoscopy has emerged as a transformative tool capable of augmenting diagnostic precision and reducing dependence on conventional histopathology.
AIM
To determine the diagnostic accuracy of AI in predicting colorectal polyp histology during real-time colonoscopy.
METHODS
A comprehensive literature search was conducted in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines and prospectively registered in International Prospective Register of Systematic Reviews (No. CRD420251012404). Nine eligible studies underwent critical appraisal using the Quality Assessment of Prognostic Accuracy Studies-2 framework. Diagnostic performance metrics - including sensitivity, specificity, positive predictive values and negative predictive values, and relative risk - were synthesized using random-effects modeling to account for between-study variability, using R software.
RESULTS
The meta-analysis incorporated 3245 patients encompassing 4752 polyps. Pooled analysis demonstrated that AI achieved an overall diagnostic accuracy of 93%, compared to 82% for human experts (relative risk = 1.13; 95% confidence interval: 1.07-1.20; P < 0.0001). AI consistently outperformed human endoscopists, particularly in cohorts involving less experienced operators or suboptimal imaging conditions. Substantial heterogeneity was observed (I2 = 74.3%), attributed to methodological differences in imaging modalities, AI architectures, and operator proficiency.
CONCLUSION
AI demonstrates high diagnostic accuracy for real-time colorectal polyp histology and may enhance clinical decision-making, although expert oversight remains essential for atypical or high-risk lesions.
Core Tip: Artificial intelligence enables accurate, real-time differentiation between neoplastic and non-neoplastic colorectal polyps, fulfilling the optical biopsy criteria. By supporting “resect-and-discard” strategies for diminutive lesions and providing decision assistance to less experienced endoscopists, artificial intelligence can streamline colonoscopy workflows, mitigate pathology workloads, and enhance colorectal cancer prevention programs.
