Evidence map›Paper›PMID 40564399›Full record

ArticleBioengineering (Basel, Switzerland)2025

Expert-AI Collaborative Training for Novice Endoscopists: A Path to Enhanced Efficiency.

Zhen Zhang, Bai-Sheng Chen, Ling Du, Quan-Lin Li, Yan Zhu, Pei-Yao Fu, Wen-Zheng Qin, Huan-Kai Shou, Ping-Ting Gao, Xin-Yang Liu and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Zhen ZhangEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Bai-Sheng ChenEndoscopic Center, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 201104, China.ORCID 0000-0001-9096-461X
Ling DuEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Quan-Lin LiEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Yan ZhuEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Pei-Yao FuEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Wen-Zheng QinEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Huan-Kai ShouEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Ping-Ting GaoEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Xin-Yang LiuEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Meng-Jiang HeEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Zi-Han GengEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Shuo WangEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.ORCID 0000-0002-2947-8783
Ping-Hong ZhouEndoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.ORCID 0000-0002-5434-0540

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEsophagogastroduodenoscopy (EGD) is essential for diagnosing upper gastrointestinal disorders. Traditional training for novice endoscopists is often inefficient and inconsistent. This study evaluates the effectiveness of an AI-assisted system (EndoAdd) in improving EGD training.

methodsIn a randomized controlled trial, eight novice endoscopists were assigned to either the EndoAdd group or a control group (traditional training). The EndoAdd system provided real-time feedback on blind spots and photodocumentation. Primary outcomes were the number of blind spots, with secondary outcomes including examination time, lesion detection, and photodocumentation completeness.

resultsThe EndoAdd system exhibited an overall accuracy of 98.0% and a mean area under the curve (AUC) of 0.984. The EndoAdd group had significantly fewer blind spots, improved photodocumentation, and a higher lesion detection rate. Examination time was reduced without compromising diagnostic accuracy.

conclusionsThe AI-assisted EndoAdd system improved novice endoscopist performance, reducing blind spots and enhancing lesion detection. AI systems like EndoAdd show potential in accelerating endoscopy training and improving procedural quality.

Indexed as

artificial intelligencenovice endoscopiststraining

Identifiers

PMID40564399
PMCPMC12189645

What OpenQuestion holds

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LicenceCC BY
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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.