ArticleClinical endoscopy2026
Impact of real-time artificial intelligence integration on detection of gastric lesions: an exploratory single-center before-and-after study using low-definition routine endoscopy.
Article in Clinical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial intelligence in upper gastrointestinal endoscopy: what detection rates cannot tell us.Clinical endoscopy · 2026Article
- Expanding role of artificial intelligence in gastric neoplasms: moving beyond resource limitations.Clinical endoscopy · 2026Article
Corrections and comments
- Commented on by
Authors and funding
11 authors.
Funding
Abstract
backgroundEarly detection of gastric neoplasia, particularly subcentimeter lesions, using upper gastrointestinal (GI) endoscopy remains challenging. This study evaluated the impact of a real-time artificial intelligence (AI) detection system on the lesion detection rate (LDR) during routine upper GI endoscopy performed using a low-definition platform commonly used in resource-limited settings, with a focus on lesions ≤0.5 cm.
methodsDiagnostic upper GI endoscopies performed between September 2024 and May 2025 were analyzed. LDRs were compared between the pre- and post-AI periods, including subgroup analyses by lesion size and type.
resultsA total of 2,329 patients were included (1,491 pre-AI, 838 post-AI). After AI implementation, overall LDR per person increased from 1.15±0.45 to 1.20±0.57 (p<0.05). Detection of lesions ≤0.5 cm increased from 18.0% to 19.8% (p<0.05), while detection of larger lesions remained unchanged. The biopsy rate decreased from 13.8% to 8.5% (p<0.05).
conclusionsReal-time AI modestly improved the detection of diminutive gastric lesions while reducing unnecessary biopsies without compromising malignancy detection, thereby supporting its utility in routine endoscopy under resource-limited conditions.
Indexed as
Identifiers
What OpenQuestion holds
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.