Liu ZG, Xie SS. Expanding the role of radiomics and artificial intelligence in the management of inflammatory bowel disease: Insights, opportunities, and challenges. World J Gastroenterol 2025; 31(42): 112107 [DOI: 10.3748/wjg.v31.i42.112107]
Corresponding Author of This Article
Shan-Shan Xie, Professor, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333 Binsheng Road, Binjiang District, Hangzhou 310052, Zhejiang Province, China. sxie@zju.edu.cn
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Gastroenterology & Hepatology
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Editorial
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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/
Nov 14, 2025 (publication date) through Nov 16, 2025
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World Journal of Gastroenterology
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1007-9327
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Liu ZG, Xie SS. Expanding the role of radiomics and artificial intelligence in the management of inflammatory bowel disease: Insights, opportunities, and challenges. World J Gastroenterol 2025; 31(42): 112107 [DOI: 10.3748/wjg.v31.i42.112107]
World J Gastroenterol. Nov 14, 2025; 31(42): 112107 Published online Nov 14, 2025. doi: 10.3748/wjg.v31.i42.112107
Expanding the role of radiomics and artificial intelligence in the management of inflammatory bowel disease: Insights, opportunities, and challenges
Zhi-Gang Liu, Shan-Shan Xie
Zhi-Gang Liu, Shan-Shan Xie, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, Zhejiang Province, China
Zhi-Gang Liu, Shan-Shan Xie, Zhejiang Key Laboratory of Neonatal Diseases, Hangzhou 310052, Zhejiang Province, China
Zhi-Gang Liu, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
Shan-Shan Xie, Department of Cell Biology, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang Province, China
Co-corresponding authors: Zhi-Gang Liu and Shan-Shan Xie.
Author contributions: Liu ZG and Xie SS jointly supervised the study and contributed equally as co-corresponding authors to the conception, organization, and final approval of the manuscript.
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: Shan-Shan Xie, Professor, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333 Binsheng Road, Binjiang District, Hangzhou 310052, Zhejiang Province, China. sxie@zju.edu.cn
Received: July 17, 2025 Revised: September 2, 2025 Accepted: October 13, 2025 Published online: November 14, 2025 Processing time: 118 Days and 22.6 Hours
Abstract
Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, remains a chronic management challenge despite the success of biological therapies such as infliximab. A major limitation is secondary loss of response, which affects a substantial proportion of patients and complicates long-term treatment strategies. Emerging technologies such as radiomics, which converts medical images into quantitative features, and artificial intelligence (AI), which integrates complex multimodal data, offer new opportunities to predict treatment response, monitor disease activity, and personalize therapy. By combining imaging-derived radiomic features with clinical and laboratory information, AI-driven models can provide early, actionable insights to guide therapeutic decisions. This editorial discusses the promise and limitations of these approaches, emphasizing how they can be integrated into clinical decision-making pathways. While challenges in standardization, validation, and clinician adoption remain, radiomics and AI represent important steps toward precision medicine, with the potential to improve outcomes and optimize care for patients with IBD.
Core Tip: Radiomics and artificial intelligence are emerging as powerful tools in the management of inflammatory bowel disease (IBD), particularly in predicting secondary loss of response to biologic therapies like infliximab. By integrating imaging data with clinical and laboratory parameters, these technologies offer a more personalized, proactive approach to disease management, enabling earlier interventions and optimizing treatment plans for Crohn’s disease and ulcerative colitis. While challenges remain in standardization and clinical adoption, the potential to transform IBD care with precision medicine is significant.
