Abstract
Since its inception, the electrocardiogram (ECG) has been an essential tool in medicine. The ECG is more than a mere tracing of cardiac electrical activity; it can detect and diagnose various pathologies including arrhythmias, pericardial and myocardial disease, electrolyte disturbances, and pulmonary disease. The ECG is a simple, non-invasive, rapid, and cost-effective diagnostic tool in medicine; however, its clinical utility relies on the accuracy of its interpretation. Computer ECG analysis has become so widespread and relied upon that ECG literacy among clinicians is waning. With recent technological advances, the application of artificial intelligence-augmented ECG (AI-ECG) algorithms has demonstrated the potential to risk stratify, diagnose, and even interpret ECGs—all of which can have a tremendous impact on patient care and clinical workflow. In this review, we examine (i) the utility and importance of the ECG in clinical practice, (ii) the accuracy and limitations of current ECG interpretation methods, (iii) existing challenges in ECG education, and (iv) the potential use of AI-ECG algorithms for comprehensive ECG interpretation.
Highlights
The ECG is more than a mere tracing of cardiac electrical activity; it can detect and diagnose various pathologies including arrhythmias, pericardial and myocardial disease, electrolyte disturbances, and pulmonary disease
While the ECG tracing itself has remained relatively unchanged since its inception, our ability to leverage the “humble” ECG to detect and diagnose various pathologies continues to evolve [2]
Its use is imperative in the evaluation and management of an array of cardiovascular diseases, including arrhythmias, pericardial and myocardial disease, as well as many non-cardiac conditions including electrolyte disturbances and pulmonary disease among many others
Summary
The expansive application of the ECG has been well established since its invention and continues to evolve over a century later. One of the first clinical applications of the ECG that directly impacted patient care was its ability to identify acute myocardial injury [2] This helped clinicians to differentiate cardiac chest pain from non-cardiac chest pain mimickers. The concept of waveform changes associated with MI and myocardial injury have advanced the utility of the ECG to detect demand ischemia (e.g., exercise-induced ischemia before an episode of acute myocardial infarction) [3]. With this advancement, coronary artery disease and its impact can be detected before a potentially fatal event, leading to improvement in cardiac morbidity and overall mortality. The story behind its interpretation is not the same as its simple and routine acquisition
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