Trial reportEndoscopy2022
Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy.
Trial report in Endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07078136 (Multicenter Observational Study of a Multimodal AI Model Using EUS, White-Light Endoscopy, and Clinical Data for Diagnosis of Upper GI Mesenchymal Tumors and Risk Stratification of Gastric GISTs), which is not on this map. Cited by 30 papers, 2 of them syntheses that pooled 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.
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.
Multicenter Observational Study of a Multimodal AI Model Using EUS, White-Light Endoscopy, and Clinical Data for Diagnosis of Upper GI Mesenchymal Tumors and Risk Stratification of Gastric GISTs
Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Artificial intelligence in endoscopy and colonoscopy: a comprehensive bibliometric analysis of global research trends.Frontiers in medicine · 2025Pooled it
- Effect of virtual reality on pain and anxiety during colonoscopy: A randomized control trial.The Journal of international medical research · 2026Trial
- Artificial intelligence assistance narrows the experience gap in endoscopic reporting of gastric lesions: a prospective clinical trial.Surgical endoscopy · 2026Trial
- Development and validation of a novel YOLOv5-based artificial intelligence model for gastric mucosal lesion detection.Surgical endoscopy · 2026Article
- Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.Digestive diseases and sciences · 2026Review
- Prevention of Gastric Cancer.Medicina (Kaunas, Lithuania) · 2026Review
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
- Artificial intelligence-assisted diagnosis of rectal neuroendocrine tumors during white-light endoscopy.World journal of gastroenterology · 2025Article
- Vendor-Agnostic Vision Transformer-Based Artificial Intelligence for Peroral Cholangioscopy: Diagnostic Performance in Biliary Strictures Compared With Convolutional Neural Networks and Endoscopists.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2025Article
- Artificial intelligence in gastrointestinal surgery: A systematic review.World journal of gastrointestinal surgery · 2025Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- The application of artificial intelligence in upper gastrointestinal cancers.Journal of the National Cancer Center · 2025Review
- The artificial intelligence revolution in gastric cancer management: clinical applications.Cancer cell international · 2025Review
- A prospective and comparative study on improving the diagnostic accuracy of early gastric cancer based on deep convolutional neural network real-time diagnosis system (with video).Surgical endoscopy · 2025Article
- Artificial intelligence efficiently predicts gastric lesions,Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2024Article
- Diagnostic Accuracy of Artificial Intelligence in Endoscopy: Umbrella Review.JMIR medical informatics · 2024Review
- Efficacy of real-time artificial intelligence-aid endoscopic ultrasonography diagnostic system in discriminating gastrointestinal stromal tumors and leiomyomas: a multicenter diagnostic study.EClinicalMedicine · 2024Article
- Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation.Scientific reports · 2024Article
- Third Eye? The Assistance of Artificial Intelligence (AI) in the Endoscopy of Gastrointestinal Neoplasms.Journal of clinical medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsTo compare endoscopy gastric cancer images diagnosis rate between artificial intelligence (AI) and expert endoscopists. PATIENTS AND
methodsWe used the retrospective data of 500 patients, including 100 with gastric cancer, matched 1:1 to diagnosis by AI or expert endoscopists. We retrospectively evaluated the noninferiority (prespecified margin 5 %) of the per-patient rate of gastric cancer diagnosis by AI and compared the per-image rate of gastric cancer diagnosis.
resultsGastric cancer was diagnosed in 49 of 49 patients (100 %) in the AI group and 48 of 51 patients (94.12 %) in the expert endoscopist group (difference 5.88, 95 % confidence interval: -0.58 to 12.3). The per-image rate of gastric cancer diagnosis was higher in the AI group (99.87 %, 747 /748 images) than in the expert endoscopist group (88.17 %, 693 /786 images) (difference 11.7 %).
conclusionsNoninferiority of the rate of gastric cancer diagnosis by AI was demonstrated but superiority was not demonstrated.
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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.