Okpete UE, Byeon H. Explainable artificial intelligence for personalized management of inflammatory bowel disease: A minireview of recent advances. World J Gastroenterol 2025; 31(35): 111033 [DOI: 10.3748/wjg.v31.i35.111033]
Corresponding Author of This Article
Haewon Byeon, PhD, Associate Professor, Director, Worker’s Care and Digital Health Lab, Department of Future Technology, Korea University of Technology and Education, 1600, Chungjeol-ro, Cheonan 31253, South Korea. bhwpuma@naver.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Sep 21, 2025; 31(35): 111033 Published online Sep 21, 2025. doi: 10.3748/wjg.v31.i35.111033
Explainable artificial intelligence for personalized management of inflammatory bowel disease: A minireview of recent advances
Uchenna E Okpete, Haewon Byeon
Uchenna E Okpete, Department of Digital Anti-aging Healthcare, Inje University, Gimhae 50834, South Korea
Haewon Byeon, Worker’s Care and Digital Health Lab, Department of Future Technology, Korea University of Technology and Education, Cheonan 31253, South Korea
Author contributions: Okpete UE and Byeon H contributed to writing the article; Byeon H designed the study; Okpete UE was involved in data interpretation and developed the methodology; all authors thoroughly reviewed and endorsed the final manuscript.
Supported by National Research Foundation of Korea, No. RS-2023-00237287.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Haewon Byeon, PhD, Associate Professor, Director, Worker’s Care and Digital Health Lab, Department of Future Technology, Korea University of Technology and Education, 1600, Chungjeol-ro, Cheonan 31253, South Korea. bhwpuma@naver.com
Received: June 23, 2025 Revised: July 21, 2025 Accepted: August 19, 2025 Published online: September 21, 2025 Processing time: 89 Days and 9.3 Hours
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.
Core Tip: Personalized management of inflammatory bowel disease is essential because of its heterogeneous clinical presentations and variable treatment responses. While artificial intelligence (AI) offers powerful tools for analyzing patient data and guiding treatment, many AI models lack transparency, which limits their clinical adoption. Explainable AI addresses this issue by making AI predictions more interpretable and trustworthy. This minireview highlights recent advancements in the application of explainable AI to inflammatory bowel disease management, including its use in predicting disease progression, selecting therapies, and monitoring treatment response.
