Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma

Sep 22, 2026

This review comprehensively summarizes the opportunities and remaining obstacles for applying artificial intelligence (AI) in esophageal cancer management, emphasizing AI-assisted endoscopy in Japan where endoscopic screening and diagnosis are central. Successful clinical translation depends on building high-quality clinical databases covering nearly all cases and enabling rigorous external validation across diverse datasets, manufacturers, and image qualities. Because endoscopists’ skills substantially influence accuracy, AI should support clinicians rather than replace them. Legal and ethical country-specific considerations are also addressed, with Japan-focused insights extendable to other high-incidence regions.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Clinical role of screening endoscopy

Opportunities for AI-assisted endoscopy

High-quality data and national databases

Challenges for clinical translation

External validation across datasets

Operator dependence and supportive use

| W | J | G | O | World Journal of
Gastrointestinal Oncology | | --- | --- | --- | --- | --- | Submit a Manuscript: [https://www.f6publishing.com](https://www.f6publishing.com) World J Gastrointest Oncol 2026 January 15; 18(1): 111357 DOI: 10.4251/wjgo.v18.i1.111357 ISSN 1948-5204 (online) MINIREVIEWS Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma Ken Kurisaki, Shinichiro Kobayashi, Taro Akashi, Yasuhiko Nakao, Masayuki Fukumoto, Kaito Tasaki, Tomohiko Adachi, Susumu Eguchi, Kengo Kanetaka Specialty type: Gastroenterology and hepatology Provenance and peer review: Invited article; Externally peer reviewed. Peer-review model: Single blind Peer-review report’s classification Scientific Quality: Grade B, Grade C, Grade D Novelty: Grade B, Grade C, Grade D Creativity or Innovation: Grade B, Grade C, Grade D Scientific Significance: Grade B, Grade C, Grade C P-Reviewer: Jin Y, PhD, Associate Chief Physician, Professor, China; Liu YX, MD, PhD, Associate Chief Physician, Associate Professor, China; Zhang MX, Professor, China 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 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. 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.](Japan. skobayashi1980@gmail.com)[ skobayashi1980@gmail.com](Japan. skobayashi1980@gmail.com) 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 asthe 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. [https://dx.doi.org/10.4251/wjgo.v18.i1.111357](https://dx.doi.org/10.4251/wjgo.v18.i1.111357) 1 January 15, 2026 Volume 18 Issue 1 Kurisaki K et al. AI-assisted endoscopy for ESCC Key Words: Artificial intelligence; Esophageal cancer; Endoscopy; Deep learning; National database; Clinical translation; Multimodal artificial intelligence ©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved. Core Tip: This

FAQs

What role does AI-assisted endoscopy play in esophageal squamous cell carcinoma management?

The review highlights AI-assisted endoscopy as a way to improve endoscopic diagnosis by supporting clinicians and reducing dependence on individual operator performance.

Why are high-quality clinical databases essential for multimodal AI models?

The review states that establishing comprehensive clinical databases is essential to validate multimodal AI models and provide robust training and evaluation data.

What challenges must be addressed before AI can be effectively translated into clinical practice?

Key challenges include rigorous external validation using diverse datasets (different endoscope manufacturers and image qualities), accounting for endoscopists’ skills that affect diagnostic accuracy, and meeting country-specific legal and ethical requirements.

Sep 22, 2026

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