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.

Abstract

Background/aims: Early 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.

Methods: Diagnostic 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.

Results: A 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).

Conclusions: Real-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.