Synthetic Artificial Intelligence in Cardiology: From Generative Models to Clinical Applications

Abstract

Abstract Synthetic artificial intelligence (AI) is rapidly redefining biomedical research—yet in cardiovascular medicine, its clinical relevance remains obscure, underexplored, and underestimated. Unlike traditional AI, which interprets data, synthetic AI generates entirely new, patient-like information: from realistic ECG signals to cardiac imaging and virtual cohorts that simulate disease progression. While recent publications have addressed specific synthetic AI tools in cardiology, no prior review has comprehensively synthesized their architectures, clinical applications, and implementation challenges within a single, practice-oriented framework.This State-of-the-Art Review fills that gap. We provide a clear, critical synthesis of core architectures—Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, Transformers, Autoregressive Models, Digital Twins, and Synthetic Cohort Simulators—and map their emerging cardiovascular applications. We examine technical barriers, ethical concerns, regulatory uncertainties, and integration challenges, while anchoring the discussion in real-world clinical priorities. This review is not only a scientific analysis—it is a call to engagement. For academic researchers, it offers conceptual and technical clarity. For clinicians in resource-constrained settings, it presents synthetic AI not as abstract innovation, but as a practical opportunity to enhance diagnostic precision, optimize workflows, and extend clinical insight. Traditional AI supports cardiologists by interpreting data; synthetic AI extends this paradigm by creating new, clinically coherent information that enhances decision-making without replacing physician expertise.As investment grows and methods mature, cardiologists must shape this evolution—not as passive adopters, but as active drivers. This review invites them to take the wheel.

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