Trial reportSurgical endoscopy2026
Artificial intelligence assistance narrows the experience gap in endoscopic reporting of gastric lesions: a prospective clinical trial.
Trial report in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
1 citing paper in PubMed.
- Artificial intelligence in gastric cancer research: a bibliometric and visualized analysis from 1993 to 2026.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHigh-quality endoscopic reports are crucial for clinical decision-making in gastric lesions. However, manually generated reports often suffer from inconsistencies, resulting in incomplete lesion documentation and insufficient descriptions. The objective is to validate an artificial intelligence (AI) reporting system for improving the quality of gastric lesion documentation with a particular focus on narrowing the experience gap among endoscopists.
methodsWe retrospectively collected gastric images to develop a reporting system for gastric lesions. The system comprises several deep learning models for lesion detection, classification, and feature recognition, and was validated using 276 video clips. Both retrospective and prospective case validations were conducted to evaluate the system's clinical effectiveness, comparing the completeness of reports with and without AI assistance, particularly in describing features of suspicious lesions.
resultsIn video validation, the system identified 99.27% (274/276) of lesions, with 88.97% accuracy in neoplasm detection, achieving 93.75% sensitivity and 86.98% specificity. Retrospective analyses showed that junior endoscopists using AI reported significantly more complete lesion documentation than original reports (74.75% vs 58.08%, P < 0.001). In the prospective trial, the AI-assisted group demonstrated superior lesion reporting completeness (86.50% vs 75.11%, P = 0.001). Notably, the AI group reported all 18 identified suspicious neoplasms, while the routine group reported only 16.
conclusionsThis study confirms the efficacy of an AI reporting system in documenting gastric focal lesions, highlighting significant improvements in the completeness and accuracy of reports generated by endoscopists.
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