OR2-28 | Diagnostic Accuracy of AI Models, Stemi Criteria, and Clinician Interpretations in Acute Myocardial Infarction: A Meta-Analysis of Pooled AUC Data
Early and accurate diagnosis of acute myocardial infarction (AMI) is essential for optimal treatment. This meta-analysis aims to compare the diagnostic performance of artificial intelligence (AI) models, STEMI criteria, and clinicians using pooled area under the curve (AUC) data from several key studies.
Authors
- Ahmed Younes (ORCID: https://orcid.org/0000-0001-5172-6547)
- Ahmed K. Mahmoud
- Ibrahim Kamel (ORCID: https://orcid.org/0009-0006-9372-7613)
- Kareem Mohamed
- Benjamin Horn
Institutions
- East Carolina University (US)
- St. Elizabeth's Medical Center (US)
- Connecticut Health Foundation (US)
Publication Details
- Journal
- Journal of the Society for Cardiovascular Angiography & Interventions
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1016/j.jscai.2025.102698
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00