Artificial intelligence in inflammatory bowel disease endoscopy - a review of current evidence and a critical perspective on future challenges.

Abstract

Inflammatory bowel disease (IBD) is a chronic and relapsing immune-mediated condition with a rising global prevalence. Endoscopic diagnosis, monitoring and surveillance currently depend on individual endoscopists, introducing subjectivity, variability, delays and potential diagnostic discrepancies. Artificial intelligence (AI) is poised to transform these processes. To date, most AI applications have focused on ulcerative colitis (UC) severity assessment, demonstrating promising results in replicating human evaluation, standardizing severity evaluation and facilitating the application of more complex scoring systems. Research into AI for Crohn's disease (CD) has lagged behind UC, due to challenges such as disease heterogeneity and transmural extension; nevertheless, significant progress has been made to automate capsule endoscopy readings for CD. Beyond the grading of disease severity, AI is also being explored for tasks such as identifying dysplastic lesions, differentiating IBD from other conditions, assessing intestinal barrier permeability, guiding treatment decisions and integrating data from multiple omics, though studies in these areas remain exploratory. This review examines the current landscape of AI applications in IBD endoscopy, summarizes key studies in the field and explores the future potential of AI in IBD care.