  The widespread adoption of electronic health records and digital healthcare
data has created a demand for data-driven insights to enhance patient outcomes,
diagnostics, and treatments. However, using real patient data presents privacy
and regulatory challenges, including compliance with HIPAA and GDPR. Synthetic
data generation, using generative AI models like GANs and VAEs offers a
promising solution to balance valuable data access and patient privacy
protection. In this paper, we examine generative AI models for creating
realistic, anonymized patient data for research and training, explore synthetic
data applications in healthcare, and discuss its benefits, challenges, and
future research directions. Synthetic data has the potential to revolutionize
healthcare by providing anonymized patient data while preserving privacy and
enabling versatile applications.
