Abstract

Artificial-intelligence (AI)-driven electrocardiogram (ECG) is an emerging service with the potential to speed up diagnosis across the cardiovascular spectrum. This systematic review aggregated evidence from published research between 2017 and mid-2022 that examined how these algorithms affect the timing and accuracy of ECG interpretation in real clinical workflows. The data show that deep-learning systems can quickly and reliably flag urgent abnormalities such as long QT syndrome, atrial flutter, ST-elevation myocardial infarction, frequently equalling or exceeding the performance of board-certified cardiologists. In emergency rooms and low-resource clinics, such tools have cut makeready time by streamlining work-up and extending specialist-level evaluation to settings with few trained interpreters. Remaining hurdles include opaque decision processes, uncertainty over legal liability, compliance with privacy regulations, and inadvertent bias introduced by under-representative training cohorts. When these algorithms are adopted with clinician oversight, they consistently yielded shorter diagnostic arcs and modestly better outcomes for patients with acute coronary and arrhythmic syndromes. The review therefore calls for validation on demographically broad, longitudinal data sets, rigorous assessment of cost-effectiveness, and the prompt enactment of harmonised regulatory standards. With continued careful integration, AI-driven ECG interpretation is likely to become a foundational pillar of efficient, equitable cardiovascular care worldwide.