Evidence map›Paper›PMID 41857227›Full record

ReviewNature reviews. Gastroenterology & hepatology2026

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

Marietta Iacucci, Giovanni Santacroce, Yasuharu Maeda, Snehali Majumder, Cesare Hassan, Dennis L Shung, Ryan W Stidham, Raf Bisschops, Valery Naranjo, Enrico Grisan and 1 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Gastroenterology & hepatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Marietta IacucciAPC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland. miacucci@ucc.ie.ORCID http://orcid.org/0000-0002-3142-9550
Giovanni SantacroceAPC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland.ORCID http://orcid.org/0000-0002-0544-0414
Yasuharu MaedaAPC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland.
Snehali MajumderAPC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland.
Cesare HassanDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.
Dennis L ShungSection of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, CT, USA.ORCID http://orcid.org/0000-0001-8226-1842
Ryan W StidhamDivision of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Raf BisschopsDivision of Gastroenterology, University Hospitals Leuven, Leuven, Belgium.
Valery NaranjoInstituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain.
Enrico GrisanSchool of Engineering Computer Science and Informatics, London South Bank University, London, UK.ORCID http://orcid.org/0000-0002-7365-5652
Subrata GhoshAPC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceInflammatory Bowel DiseasesDigital HealthHumansIntelligent SystemsPrecision Medicine

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.