JMIR Publications

ABSTRACT

Synthetic data (SD) has emerged as a promising tool for advancing cardiology research by enabling data access, enhancing patient privacy, and supporting the development of machine learning models. By generating artificial patient records that reflect real-world distributions, SD can accelerate clinical research, improve model performance for rare cardiovascular conditions, and facilitate transnational collaborations that would otherwise be restricted by data sharing barriers. Despite these advantages, the increasing use of SD raises important ethical, regulatory, and methodological concerns that remain insufficiently addressed. Key challenges include assessing the validity and generalizability of synthetic datasets, understanding their limitations in representing complex and heterogeneous patient populations, and preventing the amplification of existing biases in cardiovascular care. Regulatory frameworks such as GDPR and HIPAA safeguard privacy but do not fully account for emerging risks such as re-identification or data leakage, leaving uncertainty regarding the use of SD in evidence generation for medical devices or therapeutic evaluation. Technical constraints, including the reliability of generative models and the difficulty of capturing nuanced clinical trajectories, further limit the clinical applicability of SD. As cardiology increasingly intersects with artificial intelligence and digital health technologies, ensuring rigorous methodological standards, transparent validation, and clear governance mechanisms is essential to harness SD responsibly. This Viewpoint highlights the opportunities and blind spots associated with SD and virtual patients in cardiology and underscores the need for harmonized regulatory guidance and ethical safeguards to support their meaningful integration into research and clinical practice.