This research paper explores the application of artificial intelligence (AI) in understanding Crohn's disease, a complex gastrointestinal disorder. The workshop aims to utilize AI techniques to uncover novel insights, improve diagnosis, and develop personalized treatment strategies for Crohn's disease.

Topics covered include classification and prediction models (logistic regression, SVM, neural networks), explainable AI (feature importance, decision trees, SHAP values), epidemiology, biostatistics (correlation, t-test, ANOVA, linear and logistic models), survival analysis, medical data (EMRs, UK BioBank), causal inference (RCTs, observational studies, biases), and an overview of genomics. By harnessing AI capabilities and analyzing extensive medical data, researchers can unravel the underlying mechanisms, risk factors, and interactions associated with Crohn's disease. This research aims to drive advancements in understanding and managing this debilitating disease.