Artificial intelligence in inflammatory bowel disease: Current applications and future directions

Area of Science:

• Gastroenterology and Computational Medicine
• Focus on Inflammatory Bowel Disease (IBD) management

Background:

• Inflammatory Bowel Disease (IBD) poses significant global health challenges.
• Current IBD assessment methods face limitations due to interobserver variability and inconsistencies.
• Mucosal and histological healing are crucial therapeutic targets for improved long-term outcomes.

Purpose of the Study:

• To synthesize the applications of Artificial Intelligence (AI) in Inflammatory Bowel Disease (IBD) care.
• To evaluate the performance of various AI models in disease assessment.
• To highlight AI's potential to enhance precision, reproducibility, and efficiency in IBD management.

Main Methods:

• Structured review of AI applications across diagnostic, histological, and therapeutic domains in IBD.
• Comparative analysis of machine learning (e.g., random forest, SVM) and deep learning (e.g., CNN, RNN) models.
• Summarization of reported model performance metrics, including accuracy and AUC.

Main Results:

• AI models demonstrate high accuracy (80-97%) and AUC (0.74-0.99) in IBD evaluation.
• AI significantly reduces diagnostic variability and improves the reproducibility of disease assessment.
• AI enhances early prediction of therapeutic response and streamlines clinical workflows.

Conclusions:

• AI is transforming IBD care by providing precise and reproducible disease evaluation.
• AI facilitates personalized treatment strategies and more efficient healthcare delivery.
• AI plays an expanding role in clinical trials for patient stratification and endpoint prediction.