Evidence map›Paper›PMID 39928093›Full record

ReviewAnnals of medicine2025

Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects.

Changda Lei, Wenqiang Sun, Kun Wang, Ruixia Weng, Xiuji Kan, Rui Li

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
–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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Artificial intelligence models in predicting lymph node metastasis in early gastric cancer: a systematic review and meta -analysis.Wideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques · 2026
    Pooled it
  2. Pooled it
  3. Sensitivity and specificity of endoscopic and radiographic screening for localized gastric cancer in a real-world, population-based setting.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026
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  16. Artificial Intelligence in Upper Gastrointestinal Diagnosis.The Korean journal of helicobacter and upper gastrointestinal research · 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

6 authors.

Changda LeiDepartment of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Wenqiang SunSuzhou Medical College, Soochow University, Suzhou, China.ORCID 0000-0002-9281-1737
Kun WangDepartment of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Ruixia WengDepartment of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Xiuji KanDepartment of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Rui LiDepartment of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastric cancer (GC) occupies the first few places in the world among tumors in terms of incidence and mortality, causing serious harm to human health, and at the same time, its treatment greatly consumes the health care resources of all countries in the world. The diagnosis of GC is usually based on histopathologic examination, and it is very important to be able to detect and identify cancerous lesions at an early stage, but some endoscopists' lack of diagnostic experience and fatigue at work lead to a certain rate of under diagnosis. The rapid and striking development of Artificial intelligence (AI) has helped to enhance the ability to extract abnormal information from endoscopic images to some extent, and more and more researchers are applying AI technology to the diagnosis of GC. This initiative has not only improved the detection rate of early gastric cancer (EGC), but also significantly improved the survival rate of patients after treatment. This article reviews the results of various AI-assisted diagnoses of EGC in recent years, including the identification of EGC, the determination of differentiation type and invasion depth, and the identification of borders. Although AI has a better application prospect in the early diagnosis of ECG, there are still major challenges, and the prospects and limitations of AI application need to be further discussed.

Indexed as

Artificial IntelligenceDiagnosis, Computer-AssistedEarly Detection of CancerStomach NeoplasmsHumansartificial intelligenceconvolutional neural networksdeep learningEarly gastric cancer

Identifiers

PMID39928093
PMCPMC11812113

What OpenQuestion holds

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Registered trials

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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.