Bhandari R, Gartlan J, Oppong P, Chhabra P. Artificial intelligence for inflammatory bowel disease dysplasia detection: Current evidence and future directions. Artif Intell Gastrointest Endosc 2026; 7(2): 118493 [DOI: 10.37126/aige.118493]
Corresponding Author of This Article
Puneet Chhabra, DM, MD, FASGE, FRACP, MRCP, Department of Gastroenterology, Royal Hobart Hospital, No. 48 Liverpool Street, Hobart 7000, Tasmania, Australia. puneet.pgi@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
review-article
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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/
Artificial intelligence for inflammatory bowel disease dysplasia detection: Current evidence and future directions
Ritesh Bhandari, Jack Gartlan, Philip Oppong, Puneet Chhabra
Ritesh Bhandari, Jack Gartlan, Puneet Chhabra, Department of Gastroenterology, Royal Hobart Hospital, Hobart 7000, Tasmania, Australia
Philip Oppong, Department of Gastroenterology, Southampton General Hospital, Southampton SO16 6YD, United Kingdom
Author contributions: Bhandari R, Chhabra P, Oppong P, and Gartlan J designed the research study; Bhandari R performed the literature search and wrote the initial manuscript draft; Chhabra P contributed the conceptual framework and clinical insights; Oppong P provided expert review and substantive content revisions; Gartlan J performed language editing, manuscript formatting, and reference verification; Bhandari R and Chhabra P revised the manuscript based on reviewer feedback; all authors critically reviewed and approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Puneet Chhabra, DM, MD, FASGE, FRACP, MRCP, Department of Gastroenterology, Royal Hobart Hospital, No. 48 Liverpool Street, Hobart 7000, Tasmania, Australia. puneet.pgi@gmail.com
Received: January 4, 2026 Revised: January 28, 2026 Accepted: March 6, 2026 Published online: September 8, 2026 Processing time: 243 Days and 17.9 Hours
Abstract
There is increasing interest in using artificial intelligence (AI) in the detection of dysplasia in patients with inflammatory bowel disease (IBD). However, the application of AI in this context is limited by dataset shift, lack of validation, and bias. The purpose of this review was to examine the role of AI in the detection of IBD-associated dysplasia through three different themes: Comparative performance, mechanisms of failure, and pathways for safe clinical application. Retrospective, prospective, and multicenter studies demonstrate that AI systems trained on non-IBD data perform poorly in inflamed colons, while IBD-specific AI models provide improved accuracy, however, still experience gaps in generalizability. Various biases like selection, annotation, device, and reporting biases which are further amplified by mismatch between training and deployment environments, undermine the reliability of AI and contribute to potential inequities in care. Practical approaches to improve the reliability and fairness of AI include multicenter IBD-focused datasets, consensus labelling, multimodal architectures, and structured post-deployment monitoring. By framing “beyond dataset shift” as an overarching concept, this review provides a framework for the development of methodologically rigorous, bias-aware AI that will support rather than undermine the prevention of colorectal cancer in IBD.
Core Tip: Dataset shift impairs non-inflammatory bowel disease (IBD) trained artificial intelligence’s (AI) ability in IBD dysplasia detection as they falter in inflamed colons and therefore IBD-specific models need to be developed. However, even with the IBD-trained models, various biases (selection, annotation, device) are seen which are amplified by training-deployment mismatches and affects generalizability as evident in external validation. This review compares performances, dissects these failures, and suggests a roadmap: Multicenter datasets, consensus labelling, federated learning, and multimodal models, bolstered by regulatory oversight and clinical workflows. Such integrated steps will help to generate equitable, reliable AI to enhance surveillance and avert colorectal cancer in IBD.
