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  • Perspective
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Artificial intelligence in inflammatory bowel disease: bridging innovation, implementation and impact

Nature Reviews Gastroenterology & Hepatology (2026)Cite this article

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Abstract

Artificial intelligence (AI) is rapidly transforming the management landscape of inflammatory bowel disease (IBD). While early applications in endoscopy, digital pathology and cross-sectional imaging drew substantial attention, next-generation AI systems that enable deeper disease understanding, personalized treatment and streamlined clinical workflows are now emerging. These advances encompass the multimodal integration of endoscopic, histological and molecular data (‘endo-histo-omics’); AI-assisted assessment of the intestinal barrier; remote monitoring via wearables; and the incorporation of large language models for decision-making support and patient interactions. This Perspective traces the evolution of AI in IBD from domain-specific tools to foundational platforms supporting data-driven precision medicine. We highlight validated AI applications across diagnosis, monitoring, outcome prediction and neoplasia surveillance. We also explore the expectations of key stakeholders, including clinicians, patients, regulatory bodies and industry, and discuss unresolved challenges such as explainability, integration into workflows, reimbursement and environmental sustainability. By aligning innovation with ethical and clinical priorities, AI holds the potential to redefine IBD care. Its future will be shaped by collaboration, transparency and responsible implementation, ushering in a new era of personalized, efficient and equitable care for individuals with IBD.

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Fig. 1: Harnessing AI in IBD clinical practice and trials.
Fig. 2: Next-generation AI in IBD.
Fig. 3: AI-driven ‘one health’ approach.

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Authors and Affiliations

  1. APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland

    Marietta Iacucci, Giovanni Santacroce, Yasuharu Maeda, Snehali Majumder & Subrata Ghosh

  2. Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy

    Cesare Hassan

  3. Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy

    Cesare Hassan

  4. Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, CT, USA

    Dennis L. Shung

  5. Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA

    Dennis L. Shung

  6. Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Ryan W. Stidham

  7. Division of Gastroenterology, University Hospitals Leuven, Leuven, Belgium

    Raf Bisschops

  8. Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain

    Valery Naranjo

  9. School of Engineering Computer Science and Informatics, London South Bank University, London, UK

    Enrico Grisan

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  1. Marietta Iacucci
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Contributions

M.I., G.S., Y.M. and S.G. researched data for the article, made a substantial contributions to discussion of content, wrote the article and reviewed/edited the manuscript before submission. S.M. researched data for the article, made a substantial contribution to discussion of content and wrote the article. C.H. made a substantial contribution to discussion of content, wrote the article and reviewed/edited the manuscript before submission. D.L.S., R.W.S., R.B., V.N. and E.G. made a substantial contributions to discussion of content and reviewed/edited the manuscript before submission.

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Correspondence to Marietta Iacucci.

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Nature Reviews Gastroenterology & Hepatology thanks Laurence Lovat, who co-reviewed with Ahmed El-Sayed; Yuichi Mori; Bo Shen; and Kento Takenaka for their contribution to the peer review of this work.

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A structured literature search using PubMed, Web of Science and Google Scholar was conducted to identify studies published from database inception to July 2025 related to artificial intelligence in inflammatory bowel disease (IBD). Search terms included combinations of “artificial intelligence”, “machine learning”, “IBD”, “endoscopy”, “digital pathology”, “radiomics”, “large language models” and “wearables”. Peer-reviewed original research, reviews and clinical trials addressing diagnosis, monitoring and personalized care in IBD were prioritized. Emerging literature on generative and multimodal AI was also included.

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Iacucci, M., Santacroce, G., Maeda, Y. et al. Artificial intelligence in inflammatory bowel disease: bridging innovation, implementation and impact. Nat Rev Gastroenterol Hepatol (2026). https://doi.org/10.1038/s41575-026-01190-z

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