Artificial Intelligence in ECG Interpretation: Opportunities, Challenges, and the Future Role of Cardiovascular Technologists

Abstract

Electrocardiography remains a cornerstone in the diagnosis and management of cardiovascular diseases. Despite its widespread clinical use, manual interpretation of ECGs can be time-consuming and prone to human error, particularly in high-volume or complex settings. Traditional ECG interpretation, while effective, can be limited by inter-observer variability and human fatigue, leading to diagnostic discrepancies. The integration of Artificial Intelligence— particularly machine learning (ML) and deep learning (DL) models — has revolutionized ECG interpretation by enhancing accuracy, speed, and predictive capabilities. Recent advances in artificial intelligence and machine learning have demonstrated significant potential in automating and enhancing ECG analysis, improving both the speed and accuracy of diagnosis. This review explores the current applications of AI in ECG interpretation, highlighting key opportunities such as rapid arrhythmia detection, remote monitoring through wearable devices, and workflow optimization in clinical settings. Additionally, it examines challenges including data quality, algorithm transparency, ethical considerations, and the potential impact on the professional role of cardiovascular technologists. Finally, the paper discusses the mechanisms of AI models, clinical validation, ethical implications, and the importance of technologists’ adaptation to technological advancements, emphasizing the need for continuous upskilling to effectively integrate AI tools into clinical practice. The integration of AI into cardiovascular diagnostics represents a paradigm shift, offering both innovative solutions and new professional challenges for cardiovascular technologists. It highlights how AI can complement human expertise in improving early detection of cardiovascular diseases, optimizing workflow efficiency, and supporting remote monitoring in both hospital and community settings.