Evidence map›Paper›PMID 42755000›Full record

ReviewThe Korean journal of helicobacter and upper gastrointestinal research2026

Artificial Intelligence for Esophageal Precancerous Lesions and Esophageal Cancer.

Hannah Lee, Yeong Heon Han, Jun-Won Chung, Kyoung Oh Kim, Kwang An Kwon, Jung Ho Kim

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The Korean journal of helicobacter and upper gastrointestinal research, 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

6 authors.

Hannah LeeDivision of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, College of Medicine, Gachon University, Incheon, Korea.
Yeong Heon HanDivision of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, College of Medicine, Gachon University, Incheon, Korea.
Jun-Won ChungDivision of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, College of Medicine, Gachon University, Incheon, Korea. junwonchung@daum.net.
Kyoung Oh KimDivision of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, College of Medicine, Gachon University, Incheon, Korea.
Kwang An KwonDivision of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, College of Medicine, Gachon University, Incheon, Korea.
Jung Ho KimDivision of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, College of Medicine, Gachon University, Incheon, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Esophageal cancer is associated with a relatively poor prognosis. Early detection and diagnosis are important for the survival and prognosis of patients with esophageal cancer. Recently, the development of artificial intelligence (AI) and its application in clinical medicine has led to remarkable progress in various endoscopic fields, including the detection of Barrett's esophagus and esophageal cancer. Compared to human errors induced by fatigue and impairment in diagnostic precision, progression in the field of AI, including deep learning and convolutional neural networks, has resulted in improved diagnostic accuracy. Subtle microvascular changes in lesions, visual disturbances, and fatigue, as well as varying endoscopic expertise depending on the operator, are the major causes of missing rates in detecting cancerous lesions. Specifically, the rate of missed diagnoses of esophageal squamous cell carcinoma reaches 4%-17%. In contrast, deep learning-based AI assists in the diagnosis of esophageal squamous cell carcinoma, evaluation of invasion depth, microvascular classification, and delineation of the margin of the lesion. In this review article, we investigated the development of the current AI model introduced for esophageal precancerous lesions, including Barrett's and esophageal cancer.

Indexed as

Artificial intelligenceBarrett’s esophagusEsophageal cancer

Identifiers

PMID42755000
PMCPMC13586458

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.