Most adults undergo an ECG at some point in their lives, yet the standard interpretation extracts only a fraction of the physiological information embedded in those electrical signals. A new framework is emerging that could fundamentally change how this ubiquitous test is used — not merely to detect arrhythmias, but to serve as a continuous digital biomarker for structural and functional cardiac risk across an entire lifetime.
This narrative review in Open Heart synthesizes the current evidence landscape for AI-enhanced electrocardiography (AI-ECG), cataloguing deep learning model performance across several high-stakes clinical domains. Models applied to standard 12-lead and simplified single-lead recordings have demonstrated robust diagnostic accuracy in identifying atrial fibrillation, left ventricular systolic dysfunction, hypertrophic cardiomyopathy, cardiac amyloidosis, and heart failure with preserved ejection fraction — a condition notoriously difficult to detect early. Crucially, some deep learning architectures can extract long-term cardiovascular risk signals even from tracings classified as normal by conventional criteria, suggesting that pathophysiological change precedes visible waveform abnormality. Wearable integration extends these capabilities toward population-level continuous screening.
The clinical implications are substantial, but the review is candid about the gap between algorithmic performance and real-world deployment readiness. Most validation datasets are retrospective, single-center, and demographically narrow — conditions that reliably overstate model generalizability. Prospective multicentre trials across diverse populations remain sparse, and the pathway from a high-AUC research model to a clinically integrated, regulatory-cleared tool is rarely linear. The review situates AI-ECG as a potentially paradigm-shifting platform precisely because the ECG infrastructure already exists globally; the bottleneck is evidence quality and implementation science, not hardware. For health-conscious adults, this research signals that routine ECG data collected today may carry far more predictive value than current clinical practice extracts — a longevity-relevant observation as wearable cardiac monitoring becomes mainstream.