Predicting Inflammatory Bowel Disease Flares Using Artificial Intelligence and Remote Monitoring: Toward Proactive Disease Management

Inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis, is characterized by a chronic relapsing-remitting course with unpredictable disease flares that significantly contribute to morbidity, impaired quality of life, and increased healthcare utilization. Despite advances in biologic therapies and treat-to-target strategies, disease management remains largely reactive, relying on clinical symptoms and biomarkers that often reflect established inflammation rather than anticipate disease exacerbation. Recent advances in artificial intelligence (AI) and remote monitoring technologies offer a paradigm shift toward proactive disease management. Machine learning models can integrate and analyze complex, multidimensional datasets - including clinical, biochemical, and behavioral data - to identify patterns predictive of disease flares. Concurrently, telemedicine platforms and digital health tools enable continuous collection of patient-generated health data, facilitating real-time disease monitoring. This narrative review synthesizes current evidence on AI-based predictive modeling in IBD and evaluates the role of remote monitoring tools, including patient-reported outcomes, biomarkers, and telemedicine platforms. It further explores the integration of these approaches into a unified predictive framework aimed at early identification of disease flares and timely clinical intervention. While emerging data demonstrate promising predictive performance, challenges related to validation, generalizability, and clinical implementation remain significant.