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Introduction Accurate assessment of disease activity in inflammatory bowel disease (IBD) clinical trials relies on central reading of endoscopic and histologic images. Although considered the current gold standard, this process is limited by interobserver variability, operational delays, high costs, and scalability challenges. With rapid advances in artificial intelligence (AI) and machine learning (ML), automated image analysis has emerged as a promising tool to improve the accuracy, consistency, and efficiency of endpoint assessment in clinical trials. To address this evolving field, the International Organisation for the Study of IBD (IOIBD) developed an evidence-based consensus on the appropriate use of AI-assisted evaluation of endoscopic and histologic endpoints in IBD trials. Summary This consensus initiative involved a literature review (2018–2025) on AI applications in IBD endoscopy and histopathology. Based on the available evidence, the steering committee formulated 36 statements, which were voted on by 72 IOIBD experts, with ≥80% agreement required for consensus. A total of 45 experts completed the survey, and consensus was achieved for 28 statements. The panel concluded that AI-assisted central reading could significantly enhance diagnostic accuracy, improve reproducibility, reduce costs, and accelerate image interpretation in IBD trials. Importantly, the consensus strongly favoured a hybrid model combining human expert review with AI support, rather than complete replacement of human interpretation. However, several challenges remain. These include limited external validation of AI models, concerns regarding generalizability across populations and endoscopy platforms, and dependence on high-quality human-annotated training datasets. Conclusion The IOIBD consensus supports the integration of AI/ML tools into endoscopic and histologic assessment in IBD clinical trials, emphasising AI-augmented human decision-making. Future efforts should focus on validation, regulatory guidance, and multimodal data integration, which may ultimately enable broader use of AI in both clinical research and routine IBD care.
Postoperative recurrence (POR) remains a major challenge in Crohn’s disease (CD), occurring in up to 70% of patients within the first year after intestinal resection. Current surveillance strategies—primarily ileocolonoscopy and fecal calprotectin—have limited accuracy and lack standardized monitoring algorithms. Emerging technologies are reshaping the approach to POR detection. Advanced endoscopic imaging techniques, including confocal laser endomicroscopy and endocytoscopy, allow real-time microscopic evaluation of the anastomosis and early mucosal changes. Intestinal ultrasound and cross-sectional imaging provide non-invasive transmural assessment, improving monitoring beyond mucosal disease. Simultaneously, multi-omics approaches (genomics, transcriptomics, proteomics, metabolomics, and microbiome profiling) are identifying biological pathways associated with recurrence risk. Integration of these datasets using artificial intelligence (AI) may enable predictive multimodal models that combine clinical, imaging, and molecular data. Such AI-driven strategies could allow personalized risk stratification, earlier detection of recurrence, and tailored postoperative therapy, moving Crohn’s disease management toward precision medicine. However, prospective validation and clinical workflow integration are still required.
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