Cardiovascular emergencies often hinge on rapid, accurate electrocardiogram interpretation—a skill requiring years of specialized training that remains scarce in underserved regions worldwide. This capability gap costs lives when heart attacks, arrhythmias, and conduction disorders go unrecognized in critical moments. A breakthrough artificial intelligence system called ECG-GPT now demonstrates the ability to interpret standard 12-lead electrocardiograms with diagnostic accuracy rivaling experienced cardiologists across diverse global populations. Trained on 2.9 million ECG recordings from American healthcare systems spanning two decades, the vision-based model achieved diagnostic accuracy between 93-99% across 26 clinical conditions when validated on an additional 4.1 million ECGs from seven distinct healthcare environments, including facilities in Brazil, the United Kingdom, Germany, and rural Missouri. The system excelled at detecting rhythm abnormalities like atrial fibrillation and premature contractions (accuracy 80-95%) as well as conduction problems including various heart blocks. Unlike previous AI cardiology tools requiring specific data formats, ECG-GPT works directly with standard ECG images, making it immediately deployable in existing clinical workflows without hardware modifications. This represents a significant advancement in democratizing expert cardiac interpretation, particularly valuable for emergency departments, rural hospitals, and developing healthcare systems lacking specialized cardiology expertise. However, the model's real-world impact depends on integration challenges, regulatory approval processes, and physician acceptance of AI-assisted diagnosis in high-stakes cardiac care scenarios.