Artificial Intelligence and the Evolution of the Electrocardiogram: From Cardiovascular Diagnostic Tool to Digital Biomarker

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  Figure 1. Historical evolution and development of the ECG. Since its inception with Dr. Willem Einthoven, the modern ECG has experienced various technical improvements tailored to improving diagnostic tasks and widespread deployment. The recent entry into the deep learning era represents a novel frontier to expand the rich electrical signal into transforming cardiovascular care.

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  Figure 2. Methodological framework of AI-ECG. (Left) AI models utilize supervised, unsupervised, or self-supervised learning strategies depending on data labelling and clinical goals. (Middle) Core network architectures progress from standard artificial neural networks (ANNs) to convolutional neural networks (CNNs) and advanced hierarchical models like transformers. (Right) AI deployment scales across different data input modalities, ranging from high-fidelity 12-lead signals and device-agnostic ECG images to ambulatory single-lead wearables.

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  Figure 3. The interconnected clinical continuum of AI-ECG. The applications of AI-ECG function as an integrated ecosystem where foundational automation of routine diagnostic tasks in acute settings, such as the emergency department, provides the infrastructure for broader opportunistic screening. This screening capacity, increasingly driven by wearable and single-lead platforms, enables the identification of latent diseases and long-term prognostic risk stratification. Ultimately, these interconnected stages move the field toward new frontiers in biological discovery, transforming the standard tracing into a dynamic digital biomarker for population health.

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  Figure 4. The future landscape of AI-ECG can be conceptualized along three broad pathways. 1.) The development of robust tools to interpret the black box of AI-ECG architectures. 2.) The development of large-scale foundation models that integrate diverse and informative data modalities to improve generalizability and specificity. 3.) Rigorous investigation related to clinical implementation to ensure the ability of AI-ECG to improve clinical outcomes and patient care.