Evidence map›Paper›PMID 41607745›Full record

ReviewWorld journal of gastrointestinal oncology2026

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

Abstract readReview
In one paragraph

Review in World journal of gastrointestinal oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ken KurisakiDepartment of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan.
Shinichiro KobayashiDepartment of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan. skobayashi1980@gmail.com.
Taro AkashiDepartment of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan.
Yasuhiko NakaoDepartment of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan.
Masayuki FukumotoDepartment of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan.
Kaito TasakiDepartment of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan.
Tomohiko AdachiDepartment of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan.
Susumu EguchiDepartment of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan.
Kengo KanetakaDepartment of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial intelligenceClinical translationDeep learningEndoscopyEsophageal cancerMultimodal artificial intelligenceNational database

Identifiers

PMID41607745
PMCPMC12836157

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.