Artificial Intelligence Driven Prehospital ECG Interpretation for the Reduction of False Positive Emergent Cardiac Catheterization Lab Activations: A Retrospective Cohort Study
In this single-center retrospective study, an AI-driven algorithm reduced false positive diagnoses of OMI compared to EMS clinician gestalt. Compared to AI (which missed no OMI), STEMI criteria also reduced false positives but missed 6 true OMI. External validation of these findings in prospective cohorts is indicated.
Authors
- Aaron E. Robinson (ORCID: https://orcid.org/0000-0002-9587-2973)
- Stephen W. Smith (ORCID: https://orcid.org/0000-0002-9166-9804)
- Ishmam Ibtida
- Rehan Karim
- Michael A. Puskarich (ORCID: https://orcid.org/0000-0001-6358-4670)
- H. Pendell Meyers (ORCID: https://orcid.org/0000-0001-7484-2022)
- Peter O. Baker
- Kristie A. Royce
- Gabriel A. Keller
- Shifa R. Karim
Institutions
- Baylor University (US)
- Carolinas Medical Center (US)
- University of Minnesota Medical Center (US)
- Hennepin Healthcare Research Institute (US)
- Stony Brook University (US)
Publication Details
- Journal
- Prehospital Emergency Care
- Published
- 2024-09-05
- DOI
- https://doi.org/10.1080/10903127.2024.2399218
- Citations
- 18
- Primary Topic
- Acute Myocardial Infarction Research
- Type
- article
- Field-Weighted Citation Impact
- 7.24