Advances in the Interpretation of the Electrocardiogram by Artificial Intelligence

Figure 1. The End-to-End AI-ECG Pipeline is a comprehensive workflow that converts raw electrocardiogram data into clinically actionable diagnostic insights using artificial intelligence. ECG signals are collected from various sources, including digital devices, PDF reports, and equipment from different manufacturers [11]. Digital ECG data is then preprocessed, cleaned, and normalized, noise is filtered out, and data from heterogeneous sources is formatted for machine learning. The model training phase uses deep training architectures (such as convolutional or recurrent neural networks) to learn patterns associated with cardiac pathologies, including myocardial scar, arrhythmias, and long QT syndrome. Finally, the deployment and interpretation stage deliver AI-powered findings integrated with clinical information, frequently incorporating explainable AI features that indicate which ECG segments drove the diagnosis, enabling clinicians to trust and act on the AI’s recommendations. This unified pipeline forms Software as a Medical Device (SaMD), a standalone software that performs ECG processing and interpretation independently, without being part of a hardware ECG machine.

Figure 2. Design example of a transformer-based architecture for ECG analysis and disease prediction.

Figure 3. AI ECG Infrastructure. The deep learning process links the features of certain labeled ECGs via forward/backward propagation. The resulting model can now be used for inference. An unknown ECG is forwarded to the model for identification, which results in an LVH finding.

Figure 4. Panel (A) illustrates the paradigm shift in ECG analysis enabled by AI. Traditionally, a hypothesis-driven approach is used to establish criteria, and human interpretations are used to identify known pathological markers. In contrast, the data-driven approach harnesses large-scale datasets and deep learning algorithms to detect subtle or novel patterns in ECG signals—those that may not be readily apparent through conventional clinical reasoning. These signatures are used to diagnose conditions such as left ventricular dysfunction and atrial fibrillation and identify “previvors,” individuals with a hidden yet elevated risk for future adverse events. This transformation enables earlier detection of abnormalities, improved risk stratification, and an expanded understanding of cardiac electrical activity beyond historically defined diagnostic criteria. Panel (B) illustrates the shift from conventional ECG interpretation to an integrated, AI-driven approach (dashed line), emphasizing how AI serves as a powerful adjunct to traditional methods (solid line) to enhance diagnostic accuracy and patient care [149].