Artificial Intelligence–Driven Personalized Medicine: Transforming Clinical Practice in Inflammatory Bowel Disease
Abstract
Inflammatory bowel disease 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 is emerging as a transformative force enabling standardized, accurate, and timely disease assessment and outcome prediction, including therapeutic response. Artificial intelligence–driven intestinal barrier healing assessment provides novel insights into deep healing, facilitating the discovery of novel therapeutic targets. In addition, the automated integration of multi-omics data can enhance patient profiling and personalized management strategies. The future of inflammatory bowel disease care lies in the artificial intelligence–enabled “endo-histo-omics” integrative real-time approach, harmoniously fusing endoscopic, histologic, 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.
Figure 1 AI-enabled precision medicine in IBD. This figure illustrates the innovative AI-driven framework for integrating precision medicine into IBD clinical practice. Highlighted are cutting-edge AI-enabled imaging and analysis applications across various domains. The blue section showcases examples of AI-enabled advanced endoscopy models such as Red Density (Pentax, Japan) applied to high-definition white-light endoscopy, Segment Anything Model 2 (SAM2; Meta) applied to endocytoscopy, and computer-aided imaging analysis of confocal laser endomicroscopy. The violet section focuses on advanced digital pathology tools, with examples of automated tissue segmentation, cell detection, and disease assessment. The red section emphasizes multi-omics analysis and the green section highlights the automated multispectral imaging techniques, such as multiplex immunofluorescence for assessing epithelial and vascular barrier proteins. The yellow section schematically represents the endo-histo-omics approach, which harmoniously integrates data from endoscopy, histology, and omics. All of these innovative models, when applied to patients with IBD, can facilitate accurate disease assessment and outcome prediction, including the prediction of response to therapy. Ultimately, they enable a deep molecular characterization of patients, supporting precise phenotyping and opening the door to personalized and precision medicine in IBD care.
