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September 5, 2025Open HeartOpen Access

Risk stratification of chest pain in the emergency department using artificial intelligence applied to electrocardiograms

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Authors

JHJulian S. HaimovichMKMárton KolossvaryRARidwan Alam

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Overview

Retrospective cohort study shows AI improves risk stratification of patients with chest pain, suggesting enhanced diagnostic accuracy.

Key Points

  • AI-based model predicted major cardiovascular diagnoses more accurately than standard methods.
  • CP-AI achieved an AUROC of 0.82 versus 0.79 for the Biomarker Model, indicating better predictive performance.
  • Neural network classifier used patient data and ECGs to enhance risk stratification in emergency settings.
  • AI integration could standardize assessment in emergency departments, leading to improved patient outcomes.

Cite This Study

Haimovich et al. (2025) studied this question.

synapsesocial.com/papers/68c239e5b210217d6477dc1chttps://doi.org/10.1136/openhrt-2025-003343
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Patient Stratification for Improving Acute Chest Pain Management and Mitigate ED Crowding (Preprint)2025
  2. 2Prediction of Risk of Cardiovascular Events in Patients With Stable Angina Using Artificial Intelligence: A Systematic Review2026
  3. 3Artificial Intelligence Analysis of Chest Radiographs for Predicting Major Adverse Events in Patients Visiting the Emergency Department With Acute Cardiopulmonary Symptoms2025
  4. 4Predicting mortality in cardiovascular patients using electrocardiogram data and artificial intelligence2021 · 1 citations
  5. 5Using Machine Learning to Risk Stratify Emergency Department Patients With Chest Pain but No Acute Myocardial Infarction: A Multicenter Retrospective Analysis2025