{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T12:08:40Z","timestamp":1788955720346,"version":"build-2803163510"},"reference-count":77,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/frai.2026.1837887","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:44:50Z","timestamp":1788954290000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence-assisted endoscopic diagnosis of esophageal squamous cell carcinoma"],"prefix":"10.3389","volume":"9","author":[{"given":"Jie","family":"Mao","sequence":"first","affiliation":[{"name":"Department of Thoracic Surgery, Nanchong Central Hospital","place":["Nanchong, Sichuan, China"]},{"name":"Key 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