ArticleWiener klinische Wochenschrift2026
The impact of artificial intelligence on the adenoma detection rate : Comparison between experienced, intermediate and trainee endoscopists' adenoma detection rate.
Article in Wiener klinische Wochenschrift, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- AI-assisted improvement of endoscopic diagnosis of ampullary lesions.NPJ digital medicine · 2026Article
- Evaluation of ChatGPT's responses to frequently asked questions from osteosarcoma patients: A descriptive cross-sectional study.Medicine · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Review
- Artificial Intelligence in Gastrointestinal Endoscopy: Current Advances, Clinical Integration, and Future Directions: A Narrative Review.Clinical and experimental gastroenterology · 2026Review
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is a promising tool to achieve a high adenoma detection rate (ADR). The aim of this study is to evaluate the impact of a computer-aided detection (CADe) device on the ADRs of endoscopists with different levels of expertise.
methodsData were collected from patients who underwent colonoscopy with CADe within a 12-month period. Endoscopists were divided into three groups, a trainee group (< 500 colonoscopies), an intermediate group (500-1000 colonoscopies) and an expert group (> 2000 colonoscopies). Endoscopists with the same definition of experience without CADe support served as the control cohort. For the differences in ADR between the groups a 2-sided 95% confidence interval (CI) and odds ratios (OR) were calculated.
resultsIn this study 335 patients (155 females, 177 males) with a mean age 62.1 years (SD ± 16.2 years) were included in the CADe cohort. In this cohort 508 polyps were resected. The ADRs for the groups and control groups (without CADe) were as follows: 42.9% (95% CI: 28.5-57.2%) and 21.5% (95% CI: 11.3-31.8%) in the trainee group, 41.3% (95% CI: 33.5-49.0%) and 36.8% (95% CI: 27.9-45.6%) in the intermediate group and 39.8% (95% CI: 30.9-48.8%) and 33.3% (95% CI: 26.3-40.4%) in the expert group. There were no significant differences among the CADe groups when trainees were compared to experts (p = 0.72, OR 1.13, 95% CI: 0.58-2.16) or when intermediate endoscopists were compared to experts (p = 0.81, OR 1.06, 95% CI: 0.65-1.74).
conclusionThe use of AI appears to provide an opportunity to match the ADR-based quality of colonoscopy at an early stage of endoscopy training with experts.
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