Performance of Artificial Intelligence–Powered ECG Analysis in Suspected ST-Segment Elevation Myocardial Infarction
In suspected STEMI, this AI-ECG model correctly identified nearly all patients with acute coronary obstruction and most of those without AMI. If prospectively validated, this approach could improve management of patients with suspected AMI.
Authors
- Heather Rohm
- Mehmet Yildiz (ORCID: https://orcid.org/0000-0002-4472-8461)
- Dawn Witt (ORCID: https://orcid.org/0000-0001-8211-3911)
- Christopher M VanHove
- David M. Larson
- Jenny Chambers
- Seth Bergstedt
- Ellen Cravero (ORCID: https://orcid.org/0009-0000-3035-3825)
- Greg Kerola
- Avinash Murthy
- David Farmer
- Stephen W. Smith
- Will Belzer
- Scott W. Sharkey
- H. Pendell Meyers
- Julia Harris
- Robert Herman
- Andrew Willett
- Frank Aguirre
- Timothy D. Henry
Institutions
- University of Minnesota (US)
- Allina Health (US)
- Carolinas Medical Center (US)
- Hennepin County Medical Center (US)
- Onze Lieve Vrouwziekenhuis Hospital (BE)
- Minneapolis Heart Institute Foundation (US)
- St. John's Hospital (US)
- Christ Hospital (US)
Publication Details
- Journal
- JACC Advances
- Published
- 2026-03-20
- DOI
- https://doi.org/10.1016/j.jacadv.2026.102671
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00