ArticleCureus2026
Usefulness of Artificial Intelligence for Transnasal Esophagogastroduodenoscopy in Clinical Practice.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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10 authors.
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Abstract
Objectives We evaluated the diagnostic performance of an artificial intelligence (AI) diagnostic system for biopsy-considered suspicious lesions during transnasal esophagogastroduodenoscopy (EGD), focusing on its negative predictive value (NPV) to demonstrate whether a negative AI result may help endoscopists make a more conservative biopsy decision in selected low-risk lesions. Methods, patients, and materials We conducted a prospective single-center observational study. Endoscopists observed the entire stomach using transnasal EGD and the AI diagnostic system (gastroAI™ model-G, AI Medical Service Inc, Tokyo, Japan). Two endoscopic images of the suspected lesion were recorded using an AI diagnostic system; biopsies were collected for histological diagnosis, regardless of the AI diagnosis. The primary endpoint was the NPV of the gastroAI™ model-G. The reference standard used for calculating the diagnostic performance of the gastroAI™ model-G was histopathological diagnosis. Results Overall, 392 patients were enrolled, and 98 specimens from 87 patients were analyzed. A total of 11 endoscopists (five experts and six non-experts) performed the examinations. Five specimens (5%) were diagnosed as adenocarcinoma. As all specimens diagnosed as "negative" using the gastroAI™ model-G were pathologically benign, the NPV of the system was 100% (95% confidence interval (CI): 84%-100%). Its sensitivity, specificity, positive predictive value, and accuracy were 100% (95% CI: 36%-100%), 34% (95% CI: 25%-45%), 8% (95% CI: 3%-17%), and 38% (95% CI: 28%-48%), respectively. Specificity and accuracy were significantly higher for endoscopists than for the model (96% vs. 34% and 94% vs. 38%, respectively; p < 0.01), whereas the model had a higher sensitivity than did endoscopists (100% vs. 60%), although the difference was not statistically significant. Conclusions This study reveals the potential clinical utility of the AI diagnostic system for transnasal EGD, particularly indicating its high NPV.
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