Evidence mapPaperPMID 41809215Full record

ReviewWorld journal of gastrointestinal pharmacology and therapeutics2026

Artificial intelligence in the management of inflammatory bowel disease: What's next?

Anthony J Bilotta, Jennifer A Trebilcock, Nicholas J Hebda, Charanpreet K Sasan, Katherine M Cooper, Abbas H Rupawala

Abstract readReview
In one paragraph

Review in World journal of gastrointestinal pharmacology and therapeutics, 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

6 authors.

Anthony J BilottaDepartment of Medicine, UMass Chan Medical School, Worcester, MA 01655, United States.
Jennifer A TrebilcockDepartment of Medicine, UMass Chan Medical School, Worcester, MA 01655, United States.
Nicholas J HebdaDepartment of Medicine, UMass Chan Medical School, Worcester, MA 01655, United States.
Charanpreet K SasanDepartment of Medicine, UMass Chan Medical School, Worcester, MA 01655, United States.
Katherine M CooperDepartment of Medicine, UMass Chan Medical School, Worcester, MA 01655, United States.
Abbas H RupawalaDepartment of Medicine, Division of Gastroenterology and Hepatology, UMass Chan Medical School, Worcester, MA 01655, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inflammatory bowel disease (IBD) is a chronic, relapsing-remitting autoimmune disorder of the gastrointestinal tract. The management of IBD is complex and requires accurate assessment of disease extent and severity which guide therapeutic decisions. Endoscopic evaluation with biopsy remains the standard for diagnosing and assessing disease activity. Additionally, other modalities such as computed tomography enterography are used for suspected small bowel involvement. However, these processes are costly, time consuming, and often rely on subjective interpretation which is influenced by clinician experience. Artificial intelligence (AI) has been used to standardize and improve efficiency in many facets of healthcare. Similarly, in the past decade, there has been growing interest in the applications of AI in the management of IBD. The applications of AI in IBD to date include automated endoscopic and histologic assessment, analysis of non-invasive imaging, discovery of novel biomarkers for the development of disease prediction models and the use of chatbots. In this article, we will discuss recent advancements in the use of AI in IBD as well as some of the practical and ethical concerns with large scale implementation of AI into clinical practice.

Indexed as

Artificial intelligenceCrohn’s diseaseDeep learningInflammatory bowel diseaseMachine learningUlcerative colitis

Identifiers

PMID41809215
PMCPMC12968628

What Socratic holds

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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 Socratic graph.