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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Oct 15, 2025; 17(10): 109792
Published online Oct 15, 2025. doi: 10.4251/wjgo.v17.i10.109792
Published online Oct 15, 2025. doi: 10.4251/wjgo.v17.i10.109792
Evaluating chat generative pretrained transformer in answering questions on endoscopic mucosal resection and endoscopic submucosal dissection
Shi-Song Wang, Hui Gao, Tian-Chen Qian, Ying Du, Lei Xu, Department of Gastroenterology, The First Affiliated Hospital of Ningbo University, Ningbo 315010, Zhejiang Province, China
Shi-Song Wang, Peng-Yao Lin, Ying Du, Health Science Center, Ningbo University, Ningbo 315010, Zhejiang Province, China
Tian-Chen Qian, Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang Province, China
Author contributions: Xu L conceived the study design; Wang SS and Gao H performed the statistical analysis; Wang SS and Du Y wrote the manuscript; Qian TC and Lin PY reviewed the manuscript; All authors approved the submitted draft.
Supported by Ningbo Top Medical and Health Research Program, No. 2023020612; the Ningbo Leading Medical & Healthy Discipline Project, No. 2022-S04; the Medical Health Science and Technology Project of Zhejiang Provincial Health Commission, No. 2022KY315; and Ningbo Science and Technology Public Welfare Project, No. 2023S133.
Institutional review board statement: Since the study did not involve human or animal data and all ChatGPT answers were public, there was no need for Ethics Committee approval.
Informed consent statement: As this study does not involve human or animal data and all ChatGPT responses are publicly accessible, informed consent was not required.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Lei Xu, MD, PhD, Department of Gastroenterology, The First Affiliated Hospital of Ningbo University, No. 59 Liuting Street, Ningbo 315010, Zhejiang Province, China. xulei22@163.com
Received: May 22, 2025
Revised: June 17, 2025
Accepted: August 27, 2025
Published online: October 15, 2025
Processing time: 145 Days and 19.4 Hours
Revised: June 17, 2025
Accepted: August 27, 2025
Published online: October 15, 2025
Processing time: 145 Days and 19.4 Hours
Core Tip
Core Tip: This study evaluated the reliability and usefulness of chat generative pretrained transformer in addressing questions related to endoscopic submucosal dissection and endoscopic mucosal resection. A set of thirty targeted questions was repeatedly entered, and responses were independently rated for accuracy, completeness, and comprehensibility. Compared with Google, chat generative pretrained transformer produced more accurate, detailed, and easier to understand answers, with consistent agreement among evaluators. The findings indicate that chat generative pretrained transformer may serve as a valuable and accessible source of medical information for both patients and healthcare professionals.