De Deo Diletta, Dal Buono Arianna, Gabbiadini Roberto, Nardone Olga Maria, Ferreiro-Iglesias Rocio, Privitera Giuseppe, Bonifacio Cristiana, Barreiro-de Acosta Manuel, Bezzio Cristina, Armuzzi Alessandro
IBD Center, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Milan, Italy.
Department of Biomedical Sciences, Humanitas University, Milan, Italy.
Front Immunol. 2025 Aug 4;16:1637159. doi: 10.3389/fimmu.2025.1637159. eCollection 2025.
Artificial intelligence (AI) is rapidly gaining traction in gastroenterology, particularly in the management of inflammatory bowel disease (IBD). Given the complexity of IBD care, AI offers the potential to enhance diagnosis, monitoring, and treatment. This review aims to summarize recent developments in AI applications for IBD and identify key challenges and opportunities for future research and clinical implementation.
A narrative literature review was conducted, incorporating recent studies utilizing AI -including machine learning (ML) and deep learning (DL) - across various aspects of IBD care.
AI has demonstrated utility in multiple domains of IBD management, including endoscopic disease activity assessment, histological evaluation, imaging interpretation, prediction of disease course, treatment response, and real-world data integration. Despite promising accuracy and utility, most models remain in early development stages and lack widespread clinical validation. Major barriers include data heterogeneity, limited generalizability, and regulatory uncertainties.
AI has significant potential to revolutionize IBD care. Continued multidisciplinary collaboration, validation in diverse clinical settings, and integration into clinical workflows are critical for realizing its full impact.
人工智能（AI）在胃肠病学领域正迅速获得认可，尤其是在炎症性肠病（IBD）的管理方面。鉴于IBD护理的复杂性，AI有望改善诊断、监测和治疗。本综述旨在总结AI在IBD应用中的最新进展，并确定未来研究和临床应用的关键挑战与机遇。
进行了一项叙述性文献综述，纳入了近期在IBD护理各个方面利用AI（包括机器学习（ML）和深度学习（DL））的研究。
AI在IBD管理的多个领域已显示出实用性，包括内镜下疾病活动度评估、组织学评估、影像解读、疾病进程预测、治疗反应预测以及真实世界数据整合。尽管准确性和实用性令人期待，但大多数模型仍处于早期开发阶段，缺乏广泛的临床验证。主要障碍包括数据异质性、有限的可推广性和监管不确定性。
AI具有变革IBD护理的巨大潜力。持续的多学科合作、在不同临床环境中的验证以及融入临床工作流程对于实现其全面影响至关重要。
