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Inflammatory bowel disease (IBD) is a group of chronic inflammatory conditions of the gastrointestinal tract resulting from an inappropriate immune response to an altered gut microbiome in genetically predisposed individuals. Endoscopy plays a central role in IBD management, aiding in diagnosis, disease staging, monitoring, and therapeutic guidance. Patients with IBD face an increased risk of colorectal neoplasia due to chronic inflammation. Artificial intelligence (AI)-based systems show promise in detecting and classifying dysplasia and neoplasia during endoscopic evaluation. While there have been several studies on the application of AI to detect and diagnose various types of neoplasia in the non-IBD population, the literature in patients with IBD is limited. We aim to summarize the current evidence on the application of AI technologies to detect IBD-associated neoplasia, highlighting potential benefits, limitations, and future directions. A comprehensive literature search was performed using the PubMed database to identify relevant studies from January 2010 to February 2025. Additional references were identified from the relevant articles' bibliographies. AI-assisted endoscopy, particularly using machine learning and deep learning techniques, has shown promise in improving lesion detection rates and supporting real-time decision-making. Computer-aided detection systems may increase the sensitivity of dysplasia identification, while computer-aided diagnosis tools can aid in lesion characterization. Early studies suggest that AI can reduce interobserver variability, improve targeting of biopsies, and potentially lead to more personalized surveillance strategies. Although clinical data specific to IBD-related neoplasia remain limited compared to sporadic colorectal neoplasia, the integration of AI into endoscopic practice holds significant potential to enhance dysplasia detection and improve patient outcomes. Continued research, validation in IBD-specific cohorts, and integration with clinical workflows are essential to realize the full impact of AI in this setting.
Urquhart et al. (Mon,) studied this question.