Evidence map›Paper›PMID 41114106›Full record

ArticleWorld journal of gastrointestinal oncology2025

Evaluating chat generative pretrained transformer in answering questions on endoscopic mucosal resection and endoscopic submucosal dissection.

Shi-Song Wang, Hui Gao, Peng-Yao Lin, Tian-Chen Qian, Ying Du, Lei Xu

Abstract read
In one paragraph

Article in World journal of gastrointestinal oncology, 2025. 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. CostWorld journal of gastrointestinal oncology · 2025
    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.

Shi-Song WangDepartment of Gastroenterology, The First Affiliated Hospital of Ningbo University, Ningbo 315010, Zhejiang Province, China.
Hui GaoDepartment of Gastroenterology, The First Affiliated Hospital of Ningbo University, Ningbo 315010, Zhejiang Province, China.
Peng-Yao LinHealth Science Center, Ningbo University, Ningbo 315010, Zhejiang Province, China.
Tian-Chen QianDepartment of Gastroenterology, The First Affiliated Hospital of Ningbo University, Ningbo 315010, Zhejiang Province, China.
Ying DuDepartment of Gastroenterology, The First Affiliated Hospital of Ningbo University, Ningbo 315010, Zhejiang Province, China.
Lei XuDepartment of Gastroenterology, The First Affiliated Hospital of Ningbo University, Ningbo 315010, Zhejiang Province, China. xulei22@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the rising use of endoscopic submucosal dissection (ESD) and endoscopic mucosal resection (EMR), patients are increasingly questioning various aspects of these endoscopic procedures. At the same time, conversational artificial intelligence (AI) tools like chat generative pretrained transformer (ChatGPT) are rapidly emerging as sources of medical information.

aimTo evaluate ChatGPT's reliability and usefulness regarding ESD and EMR for patients and healthcare professionals.

methodsIn this study, 30 specific questions related to ESD and EMR were identified. Then, these questions were repeatedly entered into ChatGPT, with two independent answers generated for each question. A Likert scale was used to rate the accuracy, completeness, and comprehensibility of the responses. Meanwhile, a binary category (high/Low) was used to evaluate each aspect of the two responses generated by ChatGPT and the response retrieved from Google.

resultsBy analyzing the average scores of the three raters, our findings indicated that the responses generated by ChatGPT received high ratings for accuracy (mean score of 5.14 out of 6), completeness (mean score of 2.34 out of 3), and comprehensibility (mean score of 2.96 out of 3). Kendall's coefficients of concordance indicated good agreement among raters (all

conclusionChatGPT provided accurate and reliable answers in response to questions about ESD and EMR. Future studies should address ChatGPT's current limitations by incorporating more detailed and up-to-date medical information. This could establish AI chatbots as significant resource for both patients and health care professionals.

Indexed as

Artificial intelligenceChat generative pretrained transformerEndoscopic mucosal dissectionEndoscopic submucosal dissectionGooglePatient education

Identifiers

PMID41114106
PMCPMC12531727

What OpenQuestion holds

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LicenceCC BY-NC
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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.