Explainable artificial intelligence for personalized management of inflammatory bowel disease: A minireview of recent advances.

Abstract

Personalized management of inflammatory bowel disease (IBD) is crucial due to the heterogeneity in disease presentation, variable therapeutic response, and the unpredictable nature of disease progression. Although artificial intelligence (AI) and machine learning algorithms offer promising solutions by analyzing complex, multidimensional patient data, the "black-box" nature of many AI models limits their clinical adoption. Explainable AI (XAI) addresses this challenge by making data-driven predictions more transparent and clinically actionable. This minireview focuses on recent advancements and clinical relevance of integrating XAI for personalized IBD management. We explore the importance of XAI in prioritizing treatment and highlight how XAI techniques, such as feature-attribution explanations and interpretable model architectures, enhance transparency in AI models. In recent years, XAI models have been applied to diagnose IBD anomalies by prioritizing the predictive features for gastrointestinal bleeding and dietary intake patterns. Furthermore, studies have revealed that XAI application enhances IBD risk stratification and improves the prediction of drug efficacy and patient responses with high accuracy. By transforming opaque AI models into interpretable tools, XAI fosters clinician trust, supports personalized decision-making, and enables the safe deployment of AI systems in sensitive, individualized IBD care pathways.