Kurisaki K, Kobayashi S, Akashi T, Nakao Y, Fukumoto M, Tasaki K, Adachi T, Eguchi S, Kanetaka K. Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma. World J Gastrointest Oncol 2026; 18(1): 111357 [PMID: 41607745 DOI: 10.4251/wjgo.v18.i1.111357]
Corresponding Author of This Article
Shinichiro Kobayashi, MD, PhD, Associate Professor, FACS, Department of Surgery, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan. skobayashi1980@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
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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/
Jan 15, 2026 (publication date) through Feb 3, 2026
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Journal Information of This Article
Publication Name
World Journal of Gastrointestinal Oncology
ISSN
1948-5204
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Kurisaki K, Kobayashi S, Akashi T, Nakao Y, Fukumoto M, Tasaki K, Adachi T, Eguchi S, Kanetaka K. Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma. World J Gastrointest Oncol 2026; 18(1): 111357 [PMID: 41607745 DOI: 10.4251/wjgo.v18.i1.111357]
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, Department of Surgery, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan. skobayashi1980@gmail.com
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
Abstract
This review comprehensively summarized the potential of artificial intelligence (AI) in the management of esophageal cancer. It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis. For the clinical adaptation of AI, several challenges remain for its effective translation. The establishment of high-quality clinical databases, such as the National Clinical Database and Japan Endoscopy Database in Japan, which covers almost all cases of esophageal cancer, is essential for validating multimodal AI models. This requires rigorous external validation using diverse datasets, including those from different endoscope manufacturers and image qualities. Furthermore, endoscopists’ skills significantly affect diagnostic accuracy, suggesting that AI should serve as a supportive tool rather than a replacement. Addressing these challenges, along with country-specific legal and ethical considerations, will facilitate the successful integration of multimodal AI into the management of esophageal cancer, particularly in endoscopic diagnosis, and contribute to improved patient outcomes. Although this review focused on Japan as a case study, the challenges and solutions described are broadly applicable to other high-incidence regions.
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.
