ArticleJournal of Korean medical science2025
AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.
Article in Journal of Korean medical science, 2025. 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.
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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.
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Who cites it
1 citing paper in PubMed.
- External validation of ECG artificial intelligence for emergency and cardiac assessment across a large-scale U.S. healthcare system.NPJ digital medicine · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%,
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Registered trials
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