This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Author contributions: Fogas CR wrote the original draft and provided important intellectual contributions; Balassone V participated in a comprehensive revision of the draft and refined the final draft. All authors have read and approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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/
Received: October 15, 2025 Revised: November 16, 2025 Accepted: November 27, 2025 Published online: December 8, 2025 Processing time: 56 Days and 1.2 Hours
Abstract
We read the recent minireview by Ding et al. This review provided a structured introduction to the applications of artificial intelligence (AI) in gastrointestinal endoscopy while emphasizing the technical solutions for imaging hurdles. However, we identified some areas that were lacking analytical depth. Specifically, the review oversimplified machine learning and deep learning models (e.g., generative adversarial networks misclassification) and failed to deeply analyze the explanations for missed tumor rates and the critical role of data quality/bias. In this article, we stress that the potential of AI extends beyond diagnostics and highlight its emerging and crucial role in endoscopist training, skill development, and proficiency enhancement. We conclude that future AI adoption depends on robust multicenter trials and the implementation of AI-assisted educational platforms.
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.
