Evidence map›Paper›PMID 40177429›Full record

ArticleCureus2025

Effectiveness of Artificial Intelligence in Screening Esophagogastroduodenoscopy.

Kyoichi Adachi, Yuri Ebisutani, Yuko Matsubara, Eiko Okimoto, Norihisa Ishimura, Shunji Ishihara

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cureus, 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

6 authors.

Kyoichi AdachiHealth Center, Shimane Environment and Health Public Corporation, Matsue, JPN.
Yuri EbisutaniHealth Center, Shimane Environment and Health Public Corporation, Matsue, JPN.
Yuko MatsubaraHealth Center, Shimane Environment and Health Public Corporation, Matsue, JPN.
Eiko OkimotoHealth Center, Shimane Environment and Health Public Corporation, Matsue, JPN.
Norihisa IshimuraSecond Department of Internal Medicine, Faculty of Medicine, Shimane University, Izumo, JPN.
Shunji IshiharaSecond Department of Internal Medicine, Faculty of Medicine, Shimane University, Izumo, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis retrospective study investigated the usefulness of the EW10-EG01 artificial intelligence (AI) application for screening esophagogastroduodenoscopy (EGD). METHODOLOGY: A total of 7,655 subjects (4,863 men, 2,792 women; mean age 54.9±10.1 years) who underwent EGD during a medical checkup were enrolled in the study. The number of diagnosed upper gastrointestinal tumors was compared between EGD examinations performed with and without the AI system.

resultsEGD examinations with and without the AI system were performed on 3,841 and 3,814 subjects, respectively. Biopsy procedures were more frequently performed, and examination time was longer in EGD with AI applications than in those without. Upper gastrointestinal tumors diagnosed by EGD with and without AI were 39 (1.02%) and 24 (0.63%), respectively (

conclusionsUsage of the EW10-EG01 AI system may be useful for screening EGD due to the increased esophageal and gastric tumor detection rate.

Indexed as

aiendoscopyesophageal cancergastric cancerscreening

Identifiers

PMID40177429
PMCPMC11962169

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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 OpenQuestion graph.