VIEW AN ABSTRACT

In clinical cardiology, creating high-quality training data for artificial intelligence often requires laborious, beat-by-beat annotation by experts — a costly and time-consuming process. Self-supervised learning (SSL) offers an alternative: methods that allow AI models to learn from large collections of unlabeled or coarsely labeled ECG recordings by solving surrogate (“pretext”) tasks. Through such pretraining, models capture the underlying structure of rhythms and waveforms, enabling rapid adaptation to clinical applications such as arrhythmia detection, patient identification, or reconstruction of missing leads with only a small set of precise labels.
For cardiologists, the impact is immediate: AI can be trained on routine ECG archives with minimal expert input while producing more robust and explainable outputs. Instead of remaining a “black box,” SSL-based models reveal rhythm dynamics that align with recognizable physiological patterns, reducing annotation workload and strengthening confidence in AI-assisted decisions.
At the same time, SSL signals a broader shift in clinical AI. By uncovering latent structure and causal relationships, these models move beyond classification toward reasoning about signals. This opens the door to explainable predictions, zero-shot adaptation to new patient populations, and even hypothesis generation about arrhythmia mechanisms. Approximate labels and existing ECG archives are thus not only sufficient for useful tools today, but also the foundation for tomorrow’s AI systems that support genuine clinical reasoning.