2025/07/23 by Aart Mookhoek, Pieter Sinonque, Mariangela Allocca +19 · 1 voice
Biochemistry, Genetics and Molecular Biology · Medicine · #Inflammatory Bowel Disease #Colorectal Cancer Screening and Detection #Pancreatic and Hepatic Oncology Research
paper · pdf · doi:10.1093/ecco-jcc/jjaf133
openalex publication_date 2025/07/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
In this review, a comprehensive overview of the current state of artificial intelligence (AI) research in inflammatory bowel disease (IBD) diagnostics in the domains of endoscopy, radiology, and histology is presented. Moreover, key considerations for the development of AI algorithms in medical image analysis are discussed. AI presents a potential breakthrough in real-time, objective, and rapid endoscopic assessment, with implications for predicting disease progression. It is anticipated that, by harmonizing multimodal data, AI will transform patient care through early diagnosis, accurate patient profiling, and therapeutic response prediction. The ability of AI in cross-sectional medical imaging to improve diagnostic accuracy, automate and enable objective assessment of disease activity, and predict clinical outcomes highlights its transformative potential. AI models have consistently outperformed traditional methods of image interpretation, particularly in complex areas such as differentiating IBD subtypes, identifying disease progression, and complications. The use of AI in histology is a particularly dynamic research field. Implementation of AI algorithms in clinical practice is still lagging, a major hurdle being the lack of a digital workflow in many pathology institutes. Adoption is likely to start with implementation of automatic disease activity scoring. Beyond matching pathologist performance, algorithms may teach us more about the pathophysiology of IBD. While AI is set to substantially advance IBD diagnostics, various challenges such as heterogeneous datasets, retrospective designs, and assessment of different endpoints must be addressed. Implementation of novel standards of reporting may drive an increase in research quality and overcome these obstacles.