SynthesisDiseases of the esophagus : official journal of the International Society for Diseases of the Esophagus2023
The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis.
Synthesis in Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 5 of them syntheses that pooled it.
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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.
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Who cites it
27 citing papers in PubMed, 5 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Diagnostic Performance of Artificial Intelligence in Detecting and Distinguishing Pancreatic Ductal Adenocarcinoma via Computed Tomography: A Systematic Review and Meta-Analysis.Journal of imaging informatics in medicine · 2026Pooled it
- Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types.Cancer medicine · 2025Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Diagnostic performance of AI-assisted endoscopy diagnosis of digestive system tumors: an umbrella review.Frontiers in oncology · 2025Pooled it
- Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis.La Radiologia medica · 2024Pooled it
- Review
- A new paradigm for esophageal disease management: the application and future prospects of Shared Decision Making (SDM).World journal of surgical oncology · 2026Review
- Artificial Intelligence-Assisted Confocal Laser Endomicroscopy for Predicting Invasion Depth of Superficial Esophageal Mucosal Lesions: A Cohort Study.Clinical and translational gastroenterology · 2026Article
- Clinical characteristics of synchronous and metachronous superficial esophageal squamous cell carcinoma during surveillance after endoscopic submucosal dissection.Surgical endoscopy · 2026Article
- Esophageal cancer: from pathogenesis to precision therapies.Signal transduction and targeted therapy · 2026Review
- Evolving challenges in the management of early esophageal adenocarcinoma: from clinical staging to molecular insights.World journal of surgical oncology · 2026Review
- Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma.World journal of gastrointestinal oncology · 2026Review
- Intelligent therapeutic robotic-assisted surgery as the next frontier of precision oncology.Frontiers in oncology · 2026Review
- Applications of artificial intelligence in postoperative surveillance and management of esophageal squamous cell carcinoma.Frontiers in artificial intelligence · 2026Review
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Application of Large Language Models in Data Analysis and Medical Education for Assisted Reproductive Technology: Comparative Study.JMIR formative research · 2025Article
- Future of image enhanced endoscopy of esophageal adenocarcinoma.Clinical endoscopy · 2025Review
- Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).Oncology letters · 2025Review
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- Machine Learning of Endoscopy Images to Identify, Classify, and Segment Sinonasal Masses.International forum of allergy & rhinology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.
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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.