Evidence map›Paper›PMID 38992406›Full record

ReviewClinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association2025

How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease.

Anna L Silverman, Dennis Shung, Ryan W Stidham, Gursimran S Kochhar, Marietta Iacucci

Abstract readReview
In one paragraph

Review in Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  9. The global burden of inflammatory bowel disease: from 2025 to 2045.Nature reviews. Gastroenterology & hepatology · 2025
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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

5 authors.

Anna L SilvermanDivision of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Scottsdale, Arizona. Electronic address: silverman.anna@mayo.edu.
Dennis ShungSection of Digestive Diseases, Department of Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut.
Ryan W StidhamDivision of Gastroenterology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, Michigan; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan; Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan.
Gursimran S KochharDivision of Gastroenterology, Hepatology, and Nutrition, Allegheny Health Network, Pittsburgh, Pennsylvania.
Marietta IacucciUniversity of Birmingham, Institute of Immunology and Immunotherapy, Birmingham, United Kingdom; College of Medicine and Health, University College Cork, and APC Microbiome Ireland, Cork, Ireland.

Funding

Automated Measurement of Bowel Damage Using Enterography Imaging to Predict Clinical Outcomes in Crohn’s Disease.R01DK124779 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI STIDHAM, RYAN WILLIAM · 2020 to 2023
$2.1M
Deep Learning Approaches to Risk Stratification in Acute Gastrointestinal BleedingK23DK125718 · NIDDK · YALE UNIVERSITY · PI SHUNG, DENNIS · 2021 to 2025
$969k
NIDDK NIH HHS K23 DK125718NIDDK NIH HHS R01 DK124779
6 · The paper itself

Abstract

Artificial intelligence (AI) refers to computer-based methodologies that use data to teach a computer to solve pre-defined tasks; these methods can be applied to identify patterns in large multi-modal data sources. AI applications in inflammatory bowel disease (IBD) includes predicting response to therapy, disease activity scoring of endoscopy, drug discovery, and identifying bowel damage in images. As a complex disease with entangled relationships between genomics, metabolomics, microbiome, and the environment, IBD stands to benefit greatly from methodologies that can handle this complexity. We describe current applications, critical challenges, and propose future directions of AI in IBD.

Indexed as

Artificial IntelligenceInflammatory Bowel DiseasesHumansArtificial IntelligenceComputer-aided DiagnosisComputer VisionCrohn’s DiseaseInflammatory Bowel DiseasesMachine LearningUlcerative Colitis

Identifiers

PMID38992406
PMCPMC11719376

What OpenQuestion holds

Textmetadata
LicenceTDM
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.