TDNet Discover

Background Electrocardiogram (ECG) interpretation is a critical skill in cardiology. Traditional methods rely on physicians manually analyzing complex waveforms, which can be time-consuming and prone to variability. This creates a need for faster and more consistent methods, particularly with the increasing volume of ECG data from ambulatory and wearable devices. Purpose This study aimed to compare the performance of artificial intelligence (AI) models to that of physicians in interpreting ECGs, particularly in detecting arrhythmias. Methods The accuracy in detecting different heart arrhythmias in several AI-powered ECG analysis algorithms were evaluated and compared to the performance of board-certified physicians. A dataset of 98,951 ECGs with confirmed diagnoses was used to measure the sensitivity, specificity, and accuracy of both the AI models and physician readers. Results Artificial Intelligence (AI) algorithms showed high sensitivity and specificity for various arrhythmias, indicating their potential for reliable detection. Artificial Intelligence (AI) models also reduced the time to diagnosis across studies. Artificial Intelligence-guided screening in the Hill trial reduced the median time to AF diagnosis by 53%.11 Similarly, the Chang study reduced the mean time to STEMI diagnosis by 43% and the time to arrhythmia diagnosis by 50%.12 The Johnson study showed a 75% reduction in time to critical arrhythmia detection.13 Additionally, AI models exhibited a lower false negative rate and a high negative predictive value. Accuracy, sensitivity, and specificity were comparable to or better than physicians. Specific AI models v surpassed physicians in arrhythmia classification. These findings suggest that AI-powered ECG analysis can enhance both diagnostic accuracy and efficiency, resulting in earlier interventions. Conclusion AI-powered ECG analysis can improve efficiency and accuracy in arrhythmia detection and overall ECG interpretation compared to traditional manual physician analysis. These models may be beneficial for automating ECG interpretation, reducing diagnostic delays, and aiding physicians in making more informed decisions. Further research is needed to validate these findings in diverse clinical settings and establish optimal integration into clinical workflows.