[Heart and innovation: application of synthetic artificial intelligence in cardiology]

Synthetic artificial intelligence (AI) represents a paradigm shift in cardiovascular medicine: from prediction to creation. Unlike conventional AI, which analyzes existing data, synthetic AI generates new, patient-like clinical content, signals, images, virtual populations, and personalized treatment simulations, based on learned patterns. This review provides clinicians with a clear and practical overview of core generative technologies, including generative adversarial networks, variational autoencoders, transformers, diffusion models, autoregressive architectures, digital twins, and synthetic cohort simulators. Each method is illustrated through real-world cardiovascular use cases, from ECG generation to risk stratification, virtual trial design, and procedural planning. Beyond technical explanation, the article explores current regulatory, ethical, and validation challenges, highlighting how synthetic AI can enhance diagnostics, training, and clinical decision-making, particularly in data-limited settings. Far from replacing physicians, these tools extend clinical reach and invite the medical community to take an active role in shaping their responsible use. Synthetic AI is no longer a theoretical innovation, it is a growing infrastructure. Understanding its capabilities, limitations, and clinical relevance will be essential for the cardiologist of tomorrow.