The Evolving Paradigm of Myocardial Infarction in the Era of Artificial Intelligence.

Abstract

The classification and treatment of myocardial infarction (MI) have evolved significantly over the past few decades, with the ST-segment elevation myocardial infarction (STEMI)/non-STEMI (NSTEMI) paradigm dominating clinical practice. While STEMI, identified by ST-segment elevation (STE) on electrocardiogram (ECG), has been the hallmark for urgent reperfusion therapy, this model misses a substantial number of patients with occlusive myocardial infarction (OMI) who do not exhibit STE. Recent evidence reveals that up to 25% of NSTEMI patients have OMI, leading to higher mortality due to delayed reperfusion. The emerging OMI/NOMI (Occlusive vs. Non-Occlusive MI) paradigm offers a more nuanced approach, incorporating advanced ECG interpretation and tools like point-of-care echocardiography and artificial intelligence (AI). AI has shown promise in detecting subtle ECG changes indicative of OMI, improving diagnostic accuracy and reducing misdiagnosis. This paradigm shift has important implications for clinical practice, calling for earlier identification of OMI and more inclusive treatment strategies to enhance patient outcomes.