Evidence map›Paper›PMID 33122593›Full record

ReviewSurgical laparoscopy, endoscopy & percutaneous techniques2020

A Gratifying Step forward for the Application of Artificial Intelligence in the Field of Endoscopy: A Narrative Review.

Yixin Xu, Yulin Tan, Yibo Wang, Jie Gao, Dapeng Wu, Xuezhong Xu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Surgical laparoscopy, endoscopy & percutaneous techniques, 2020. 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

6 authors.

Yixin XuDepartment of General Surgery, Changzhou Wujin People's Hospital Affiliated to Jiangsu University, The Wujin Clinical College of Xuzhou Medical University, Changzhou.
Yulin TanDepartment of General Surgery, Changzhou Wujin People's Hospital Affiliated to Jiangsu University, The Wujin Clinical College of Xuzhou Medical University, Changzhou.
Yibo WangDepartment of General Surgery, Changzhou Wujin People's Hospital Affiliated to Jiangsu University, The Wujin Clinical College of Xuzhou Medical University, Changzhou.
Jie GaoDepartment of General Surgery, Kunshan Traditional Chinese Medicine Hospital, Kunshan.
Dapeng WuDepartment of Endoscopy, Jiangsu Provincial Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu, China.
Xuezhong XuDepartment of General Surgery, Changzhou Wujin People's Hospital Affiliated to Jiangsu University, The Wujin Clinical College of Xuzhou Medical University, Changzhou.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEndoscopy is the optimal choice of diagnosis of gastrointestinal (GI) diseases. Following the advancements made in medical technology, different kinds of novel endoscopy-methods have emerged. Although the significant progress in the penetration of endoscopic tools that have markedly improved the diagnostic rate of GI diseases, there are still some limitations, including instability of human diagnostic performance caused by intensive labor burden and high missed diagnosis rate of subtle lesions. Recently, artificial intelligence (AI) has been applied gradually to assist endoscopists in addressing these issues. METHODS AND

resultsIn this study, we provide an overview of the application of the AI technology in the field of endoscopy, as well as its current limitations and prospects. The result suggested that AI had a significantly higher diagnostic accuracy compared with human endoscopists in the diagnosis of GI diseases. With the help of AI technology, the diagnostic performances of nonexpert endoscopists had been improved considerably.

conclusionAI technology still has several limitations, and there are still plenty of opportunities for improvement.

Indexed as

Artificial IntelligenceGastrointestinal DiseasesEndoscopyHumans

Identifiers

PMID33122593
PMCPMC8132898

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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.