Artificial intelligence in inflammatory bowel disease: Current applications and future directions

Inflammatory bowel disease (IBD) represents a major global health concern, significantly impacting patient quality of life and healthcare systems. Mucosal and histological healing have emerged as key therapeutic targets, offering better long-term outcomes compared with previous targets. However, accurate disease assessment remains challenging because of interobserver variability and inconsistencies between endoscopic and histological findings. Artificial intelligence (AI) is transforming IBD care by enhancing the precision and reproducibility of disease evaluation. This review provided a structured synthesis of AI applications in IBD, organized by diagnostic, histological, and therapeutic domains, and highlighted comparative model performance such as machine learning classifiers (random forest, support vector machine) and deep learning models (convolutional and recurrent neural networks) with reported accuracy between 80% and 97% and areas under the curve ranging from 0.74 to 0.99. Beyond summarizing existing tools, the review emphasized the ability of AI to reduce diagnostic variability, improve early prediction of therapeutic response, and streamline clinical workflows. These advancements support a shift toward personalized treatment strategies and more efficient care delivery. Additionally, we outlined the expanding role of AI in clinical trials in which it supports patient stratification, endpoint prediction, and automated data integration.