Agentic Artificial Intelligence in Inflammatory Bowel Disease: Toward Autonomous and Adaptive Care

Abstract

Inflammatory bowel disease (IBD) is a chronic, heterogeneous condition requiring ongoing monitoring and iterative therapeutic adjustment1. Current management remains limited by fragmented data sources and episodic clinical assessment, resulting in reactive decision-making. Although artificial intelligence (AI) has demonstrated promise in IBD, most applications remain task-specific and cross-sectional2, failing to capture the longitudinal nature of disease. Agentic AI offers a shift toward continuous and adaptive care by integrating diverse data streams into an evolving representation of disease state. This enables early detection of change and supports proactive intervention through a closed-loop system linking monitoring, interpretation, and action4. Despite its potential, challenges related to data quality, interpretability, and clinical integration must be addressed for implementation5.