Evidence map›Paper›PMID 40677567›Full record

ReviewWorld journal of gastrointestinal endoscopy2025

Revolutionizing upper gastrointestinal disease diagnosis: The transformative role of artificial intelligence in endoscopy.

Xin-Rui Li, Mo-Wei Kong, Xiang-Feng Guan, Yu Gao

Abstract readReview
In one paragraph

Review in World journal of gastrointestinal endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. 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

4 authors.

Xin-Rui LiDepartment of Cardiology, Guiqian International General Hospital, Guiyang 550018, Guizhou Province, China.
Mo-Wei KongDepartment of Cardiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan Province, China.
Xiang-Feng GuanDepartment of Cardiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan Province, China.
Yu GaoDepartment of Endocrinology, Hebei Key Laboratory of Panvascular Diseases, Affiliated Hospital of Chengde Medical University, Chengde 067000, Hebei Province, China. yugao815@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid advancement of technology, artificial intelligence (AI) has emerged as a transformative force in gastroenterology, particularly in diagnosing upper gastrointestinal diseases such as Barrett's esophagus (BE), esophageal cancer, gastroesophageal reflux disease (GERD), and esophagogastric varices. AI's capabilities in image analysis, classification, detection, and segmentation have significantly improved diagnostic accuracy and efficiency. For BE, AI models achieve high sensitivity and specificity in detecting early neoplastic changes and guiding targeted biopsies. In esophageal cancer, AI enhances early lesion detection, improving intervention success rates. For GERD, AI classifies disease severity based on the Los Angeles grading system and accurately segments lesions. Additionally, AI detects esophagogastric varices and predicts bleeding risks more effectively than traditional methods. Despite these advancements, challenges remain, including the need for high-quality data, multi-center validation, and ensuring AI model interpretability. Future research should address these issues and further integrate AI into clinical practice to optimize patient outcomes. This review highlights AI's transformative impact on upper gastrointestinal disease diagnosis, emphasizing its potential to revolutionize endoscopic practice and improve patient care.

Indexed as

Artificial intelligenceDeep learningEndoscopic diagnosisEsophageal lesionsGastroesophageal reflux diseaseUpper gastrointestinal diseases

Identifiers

PMID40677567
PMCPMC12264744

What OpenQuestion 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 OpenQuestion graph.