Evidence map›Paper›PMID 41079217›Full record

ArticleEndoscopy international open2025

Impact of interaction between an artificial intelligence endoscopic support system and endoscopists on diagnosis of gastric neoplastic lesions.

Hiroya Mizutani, Yosuke Tsuji, Dai Kubota, Hiroyuki Hisada, Yuko Miura, Daisuke Ohki, Chihiro Takeuchi, Naomi Kakushima, Nobutake Yamamichi, Ryosuke Kikuchi and 5 more

Abstract read
In one paragraph

Article in Endoscopy international open, 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. Article
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

15 authors.

Hiroya MizutaniDepartment of Gastroenterology Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.
Yosuke TsujiDepartment of Gastroenterology Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.
Dai KubotaDepartment of Gastroenterology Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.
Hiroyuki HisadaDepartment of Gastroenterology Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.
Yuko MiuraDepartment of Gastroenterology Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.
Daisuke OhkiDepartment of Gastroenterology Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.
Chihiro TakeuchiDepartment of Gastroenterology Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.ORCID 0000-0002-2746-9189
Naomi KakushimaDepartment of Gastroenterology Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.
Nobutake YamamichiDepartment of Gastroenterology Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.
Ryosuke KikuchiAI Medical Service Inc., Tokyo, Japan.
Mitsuaki IshiokaDepartment of Gastroenterology, The Cancer Institute Hospital Of JFCR, Koto-ku, Japan.
Atsuo YamadaDepartment of Gastroenterology Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.
Shinya KodashimaDepartment of Medicine Graduate School of Medicine, Teikyo University School of Medicine, Tokyo, Japan.
Tomohiro TadaAI Medical Service Inc., Tokyo, Japan.
Mitsuhiro FujishiroDepartment of Gastroenterology Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and study aims: Artificial intelligence (AI) is expected to enhance the ability of endoscopists to detect gastric neoplastic lesions; however, its effectiveness among highly skilled Japanese expert endoscopists has not been validated. We developed a novel AI-assisted diagnostic tool for detection of gastric neoplastic lesions and evaluated its utility by comparing the diagnostic performance of endoscopists with and without AI assistance. Patients and methods: Diagnostic performance of gastric neoplastic lesions without and with AI assistance was compared among 14 expert endoscopists and 12 non-expert endoscopists using an evaluation dataset consisting of 150 images containing neoplastic lesions and 350 images without lesions. A general linear mixed model was applied for comparative analysis. The primary outcome was to demonstrate superiority of sensitivity and non-inferiority of specificity among expert endoscopists using AI compared with those without AI. The significance level for sensitivity was set at 2.5% and the non-inferiority margin for specificity was defined as a log odds ratio of -0.25. Results: Our AI demonstrated superiority in sensitivity (66.4% without AI vs. 83.5% with AI; odds ratio [OR] 2.562, 97.5% confidence interval [CI] 2.069-3.172) and non-inferiority in specificity (90.8% without AI vs. 92.9% with AI; OR 1.326, 95% CI 1.122-1.565) among expert endoscopists. Conclusions: AI contributed to improved diagnostic performance even among Japanese expert endoscopists in detecting gastric neoplastic lesions. These findings suggest that the AI system may have potential to support consistently high diagnostic performance across varying levels of endoscopic expertise.

Indexed as

Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE)Endoscopy Upper GI TractPrecancerous conditions & cancerous lesions (displasia and cancer) stomach

Identifiers

PMID41079217
PMCPMC12511922

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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.