AI-driven Personalized Medicine: Transforming Clinical Practice in Inflammatory Bowel Disease

Abstract

Inflammatory bowel disease (IBD) is marked by significant clinical heterogeneity, posing challenges for accurate diagnosis and personalized treatment strategies. Conventional approaches, such as endoscopy and histology, often fail to adequately and accurately predict medium and long-term outcomes, leading to suboptimal patient management. Artificial intelligence (AI) is emerging as a transformative force enabling standardized, accurate, and timely disease assessment and outcome prediction, including therapeutic response. AI-driven intestinal barrier healing assessment provides novel insights into deep healing, facilitating the discovery of novel therapeutic targets. Additionally, the automated integration of multi-OMICs data can enhance patient profiling and personalized management strategies. The future of IBD care lies in the AI-enabled "Endo-Histo-OMICs" integrative real-time approach, harmoniously fusing endoscopic, histological, and molecular data. Despite challenges in its adoption, this paradigm shift has the potential to refine risk stratification, improve therapeutic precision, and enable personalized interventions, ultimately advancing the implementation of precision medicine in routine clinical practice.