The exponential growth of biomedical data, combined with increasingly stringent privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR), has created a significant bottleneck in the development of Artificial Intelligence (AI) and machine learning (ML) models for clinical and translational research. Synthetic data generation has emerged as a promising solution, enabling researchers to produce statistically realistic datasets that preserve the distributional properties of real patient data without exposing sensitive information. This commentary argues that GAN-based methods, and CTGAN in particular, represent a practical and scalable pathway for privacy-preserving biomedical AI: they outperform classical anonymisation techniques in downstream ML utility, handle mixed tabular data types that are pervasive in clinical records, and generalise across oncology, genomics, clinical trial simulation, and electronic health record synthesis. This paper reviews the theoretical foundations and practical applications of synthetic data generation methods in biomedical contexts, with a particular focus on Generative Adversarial Networks (GANs) and their tabular variant, the Conditional Tabular GAN (CTGAN). We further discuss emerging approaches including diffusion-based generative models and federated synthetic data generation. We examine key use cases, outline methodological considerations for validating the fidelity and utility of generated datasets, and address critical limitations including privacy leakage risks, model bias, and unresolved ethical and regulatory questions. Our analysis demonstrates that GAN-based approaches can produce synthetic biomedical records that support downstream ML tasks with accuracy comparable to models trained on real data, opening a viable pathway toward privacy-preserving, data-rich biomedical research.