ReviewFrontiers in artificial intelligence2026
Artificial intelligence-assisted endoscopic diagnosis of esophageal squamous cell carcinoma.
Review in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Esophageal squamous cell carcinoma (ESCC) remains a major cause of cancer-related mortality, and prognosis depends strongly on detection at a curable stage. Endoscopy is central to screening and diagnosis, but subtle flat lesions, operator dependence, cognitive fatigue, lesion-location blind spots, and variability in interpretation contribute to missed or delayed diagnosis. Artificial intelligence (AI), particularly deep learning applied to white-light imaging, narrow-band imaging, blue-light imaging, magnifying endoscopy, Lugol chromoendoscopy, video endoscopy, and endocytoscopy, has shown clinically meaningful potential for lesion detection, margin delineation, invasion-depth estimation, and microvascular-pattern classification. Following peer-review feedback, this article has been reframed as a structured mini-review and evidence appraisal rather than a formal systematic review or meta-analysis. We summarize representative studies published mainly from 2019 to 2026, describe the literature-identification scope and eligibility criteria, and critically appraise the evidence using domains adapted from diagnostic-accuracy, prediction-model, and medical-imaging AI reporting frameworks. In this review, multimodal AI refers specifically to complementary endoscopic inputs rather than routine integration of histopathological or genomic data into deployed ESCC endoscopic systems. Current evidence suggests that many AI systems achieve high sensitivity in enriched image datasets, and several video-based, prospective, or randomized studies support translational feasibility. However, major limitations remain: most studies are retrospective; many use single-center or high-quality still-image datasets; confidence intervals, calibration, uncertainty quantification, subgroup analysis, failure-mode reporting, and latency benchmarks are inconsistent; and external validation across devices, operators, patient spectra, and live-video workflows remains limited.
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Registered trials
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.