Copyright
©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Jan 15, 2026; 18(1): 111357
Published online Jan 15, 2026. doi: 10.4251/wjgo.v18.i1.111357
Published online Jan 15, 2026. doi: 10.4251/wjgo.v18.i1.111357
Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma
Ken Kurisaki, Shinichiro Kobayashi, Masayuki Fukumoto, Kaito Tasaki, Tomohiko Adachi, Susumu Eguchi, Kengo Kanetaka, Department of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
Taro Akashi, Yasuhiko Nakao, Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
Masayuki Fukumoto, Department of Surgical and Interventional Sciences, Faculty of Medicine and Health Sciences, McGill University, Montreal H3G 1A4, Quebec, Canada
Co-first authors: Ken Kurisaki and Shinichiro Kobayashi.
Author contributions: Kurisaki K and Kobayashi S drafted and edited the manuscript; Akashi T contributed to writing and revising the endoscopic diagnosis section; Nakao Y and Tasaki K edited the sections on the clinical application challenges of artificial intelligence and ethical and legal considerations; Adachi T assisted Fukumoto M in editing the sections on future perspectives and challenges; Eguchi S and Kanetaka K conceived the study and supervised its overall design; all authors read and approved the final manuscript.
Supported by Japan Society for the Promotion of Science, No. 24K11935.
Conflict-of-interest statement: The authors have no competing interests to declare.
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: Shinichiro Kobayashi, MD, PhD, Associate Professor, FACS, De
Received: July 1, 2025
Revised: August 15, 2025
Accepted: November 24, 2025
Published online: January 15, 2026
Processing time: 198 Days and 11.3 Hours
Revised: August 15, 2025
Accepted: November 24, 2025
Published online: January 15, 2026
Processing time: 198 Days and 11.3 Hours
Core Tip
Core Tip: This review detailed how artificial intelligence (AI) mitigates operator dependence in the endoscopic diagnosis of esophageal squamous cell carcinoma by comparing the sensitivity and specificity of innovative deep learning models with those of expert endoscopists. This further highlights the use of large-scale repositories, such as the National Clinical Database and Japan Endoscopy Database, for robust AI training and validation. Multimodal AI using big databases proposes a multi-institutional or multi-vendor AI strategy in Japan. Finally, we outlined future directions for real-time endoscopic support and the integration of clinical outcomes into next-generation AI-driven endoscopy.