Evolving Role of Artificial Intelligence in Endoscopic Management of Inflammatory Bowel Disease: Diagnosis, Surveillance, and Assessment

Abstract

Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, presents substantial diagnostic and management challenges because of its variable clinical course and the limitations of conventional endoscopy. Although endoscopic procedures are crucial for diagnosis and surveillance, their inherent subjectivity and inter-observer variability complicate disease assessment. Recent advances in artificial intelligence (AI) offer promising solutions to these challenges by enabling automated, precise, and objective image analysis. AI technologies have demonstrated success in diagnosing IBD, distinguishing it from other gastrointestinal disorders, and facilitating early identification of neoplasia in IBD patients, improving clinical decision-making and potentially reducing the need for invasive procedures. Furthermore, AI applications for evaluating endoscopic images have enhanced the accuracy of disease severity assessments such as the Mayo Endoscopic Score and Ulcerative Colitis Endoscopic Index of Severity by overcoming issues related to observer variability. Integration of AI with advanced endoscopic technologies, including image-enhanced and magnified endoscopy, further improves lesion characterization and offers insights into mucosal healing, which is crucial for optimizing treatment. While AI's potential in IBD management is substantial, challenges remain in its clinical implementation, necessitating further validation through real-world data and regulatory approval. This review explores the evolving role of AI in transforming IBD diagnosis, surveillance, and assessment, with a focus on enhancing patient care through improved precision and efficiency.

Keywords: Crohn's disease; colonoscopy; convolutional neural network; digital pathology; ulcerative colitis.