Publication Name	Artificial Intelligence in Gastrointestinal Endoscopy
Manuscript ID	115140
Country	Italy

Category	Computer Science, Artificial Intelligence
Manuscript Type	Letter to the Editor
Article Title	Artificial intelligence in gastrointestinal endoscopy: Focus on analytical depth and endoscopist training
Manuscript Source	Unsolicited Manuscript
All Author List	Cristina Rebeca Fogas and Valerio Balassone
Funding Agency and Grant Number
Corresponding Author	Valerio Balassone, MD, PhD, Digestive Endoscopic Surgery, Gastroenterology and Nutrition, Bambino Gesù Children’s Hospital, IRCCS, Piazza Sant’Onofrio 4, Rome 00165, Lazio, Italy. valerio.balassone@opbg.net
Key Words	Artificial intelligence; Gastrointestinal endoscopy; Deep learning; Endoscopy training; Skill development; Analytical depth; Data quality
Core Tip	This article highlights a recent review of the latest advancements of artificial intelligence (AI) in the field of gastrointestinal endoscopy discussed in the recent minireview by Ding et al. We raise concerns on the analytical depth of the manuscript, namely the lack of detailed analyses on missed tumor rates, machine learning model complexities, and dataset quality. We also discuss the future directions of the potential of AI in endoscopy training to facilitate skill development and enhance overall endoscopist proficiency, an area crucial for the future adoption of AI in clinical settings.
Citation	Fogas CR, Balassone V. Artificial intelligence in gastrointestinal endoscopy: Focus on analytical depth and endoscopist training. Artif Intell Gastrointest Endosc 2025; In press

Received	2025-10-15 09:00
Peer-Review Started	2025-10-15 09:00
To Make the First Decision
Return for Revision	2025-11-04 09:35
Revised	2025-11-16 18:33
Second Decision	2025-11-27 02:34
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-11-27 08:08
Articles in Press	2025-11-27 08:08
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2689-7164 (online)
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/
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