Evidence map›Paper›PMID 38947752›Full record

ReviewEndoscopic ultrasound

The application of artificial intelligence in EUS.

Deyu Zhang, Chang Wu, Zhenghui Yang, Hua Yin, Yue Liu, Wanshun Li, Haojie Huang, Zhendong Jin

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Endoscopic ultrasound. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  11. Diagnostic value of endoscopic ultrasound in staging of pancreatic cancer.World journal of gastrointestinal oncology · 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

8 authors.

Deyu ZhangDepartment of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China.
Chang WuDepartment of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China.
Zhenghui YangDepartment of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China.
Hua YinDepartment of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China.
Yue LiuDepartment of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China.
Wanshun LiDepartment of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China.
Haojie HuangDepartment of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China.
Zhendong JinDepartment of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.

Indexed as

Artificial intelligenceConvolutional neural networksDeep learningEUSGastrointestinal diseaseMachine learningPancreatic lesions

Identifiers

PMID38947752
PMCPMC11213611

What OpenQuestion holds

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LicenceCC BY
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.