Evidence map›Paper›PMID 41278155›Full record

ArticleWorld journal of gastroenterology2025

Expanding the role of radiomics and artificial intelligence in the management of inflammatory bowel disease: Insights, opportunities, and challenges.

Zhi-Gang Liu, Shan-Shan Xie

Abstract readEditorial
In one paragraph

Article in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

2 authors.

Zhi-Gang LiuChildren's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, Zhejiang Province, China.
Shan-Shan XieChildren's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, Zhejiang Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inflammatory bowel disease (IBD), encompassing Crohn's disease and ulcerative colitis, remains a chronic management challenge despite the success of biological therapies such as infliximab. A major limitation is secondary loss of response, which affects a substantial proportion of patients and complicates long-term treatment strategies. Emerging technologies such as radiomics, which converts medical images into quantitative features, and artificial intelligence (AI), which integrates complex multimodal data, offer new opportunities to predict treatment response, monitor disease activity, and personalize therapy. By combining imaging-derived radiomic features with clinical and laboratory information, AI-driven models can provide early, actionable insights to guide therapeutic decisions. This editorial discusses the promise and limitations of these approaches, emphasizing how they can be integrated into clinical decision-making pathways. While challenges in standardization, validation, and clinician adoption remain, radiomics and AI represent important steps toward precision medicine, with the potential to improve outcomes and optimize care for patients with IBD.

Indexed as

Artificial IntelligenceColitis, UlcerativeCrohn DiseaseInflammatory Bowel DiseasesPrecision MedicineClinical Decision-MakingHumansRadiomicsTreatment OutcomeArtificial intelligenceCrohn’s diseaseDisease monitoringInflammatory bowel diseaseInfliximabMachine learningPrecision medicineRadiomicsSecondary loss of responseUlcerative colitis

Identifiers

PMID41278155
PMCPMC12635749

What OpenQuestion holds

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LicenceCC BY-NC
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.