Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Advanced Development and New Horizons

Area of Science:

• Gastroenterology
• Medical Imaging
• Artificial Intelligence

Background:

• Inflammatory bowel disease (IBD), encompassing Crohn's disease and ulcerative colitis, presents complex diagnostic challenges.
• Current IBD diagnosis and management heavily rely on endoscopy, which involves subjective interpretation by endoscopists.
• The need for objective and standardized diagnostic tools in IBD is significant.

Purpose of the Study:

• To review the current applications of artificial intelligence (AI) in the endoscopic diagnosis of IBD.
• To explore the potential of AI in improving the accuracy and objectivity of IBD diagnosis and treatment.
• To discuss the challenges and future directions for integrating AI into clinical gastroenterology practice for IBD.

Main Methods:

• Systematic review of existing literature on AI applications in IBD diagnosis via gastroenteroscopy.
• Analysis of studies focusing on AI methodologies, datasets, and clinical findings in IBD.
• Discussion of AI's role in pathogenesis, etiology, diagnosis, and prognosis of IBD.

Main Results:

• AI demonstrates growing potential in various aspects of IBD management, including diagnosis.
• Large datasets are crucial for developing effective AI tools for IBD.
• Variability in AI approaches and data limits current clinical integration.

Conclusions:

• AI holds significant promise for enhancing the diagnosis and treatment of IBD patients.
• Further research and standardization are needed to overcome barriers to AI adoption in clinical practice.
• AI could revolutionize gastroenterological practice by providing objective insights into IBD.