In this paper, the authors review how artificial intelligence is being applied to inflammatory bowel disease, showing how data-driven technologies are beginning to transform the way these chronic conditions are detected, monitored, and treated.

They describe AI systems that analyze endoscopic images, histopathology slides, laboratory values, and clinical records to identify disease activity, predict flares, and guide therapy selection in Crohn’s disease and ulcerative colitis. Deep learning models are now able to score mucosal inflammation, detect subtle lesions on colonoscopy or capsule endoscopy, and quantify microscopic features in biopsies with accuracy comparable to expert clinicians, making these tools particularly valuable for standardizing disease assessment.

From a population-health and chronic-disease perspective, the implications for IBD are profound. These diseases require lifelong surveillance, frequent endoscopy, and repeated treatment adjustments, which creates high costs and large variations in care. AI enables earlier and more consistent detection of active inflammation, allowing physicians to intervene before symptoms worsen or complications develop. By predicting which patients will respond to specific biologic therapies, AI can also reduce trial-and-error prescribing, limit exposure to ineffective drugs, and improve long-term outcomes. The authors highlight how these technologies could support more efficient screening, tighter disease control, and more equitable access to specialist-level interpretation, especially in settings where gastroenterology expertise is limited. Overall, the paper shows that AI is not just improving IBD diagnostics - it is enabling a more proactive, personalized, and scalable approach to managing these lifelong diseases.