Machine Learning Approaches to Biologic Therapy Response in Inflammatory Bowel Disease: A Review Article

Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, remains a complex and heterogeneous condition with variable response to biologic therapies. Despite significant advances with anti-TNF agents, integrin inhibitors, and interleukin-12/23 blockers, treatment outcomes are limited by primary nonresponse, secondary loss of efficacy, immunogenicity, and substantial healthcare costs. The advent of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) algorithms, offers new opportunities to address these challenges by integrating diverse clinical, laboratory, imaging, histologic, genetic, and microbiome data. Recent studies demonstrate that AI-based models achieve high accuracy in predicting biologic response, remission, and adverse outcomes, outperforming conventional statistical approaches. AI has also shown promise in real-time endoscopic assessment, histologic grading, and forecasting disease progression, enabling earlier intervention and personalized therapy. However, current models are constrained by limited datasets, heterogeneity, lack of large-scale prospective validation, and concerns regarding interpretability, transparency, and patient privacy. Future efforts should prioritize multicenter trials, external validation, and ethical integration into clinical workflows. Collectively, AI-driven prediction of biologic therapy response represents a pivotal step toward precision medicine in IBD, with the potential to optimize therapeutic strategies, reduce ineffective treatment exposure, and improve patient outcomes.