The scarcity, imbalance, and privacy constraints of medical datasets remain major challenges in developing robust machine learning systems for healthcare applications. Generative Adversarial Networks (GANs) have emerged as a powerful deep learning framework capable of synthesizing realistic medical data, including medical images, electronic health records (EHRs), and physiological signals. This study explores the application of GAN-based models for synthetic medical data generation and data augmentation to improve diagnostic accuracy, model generalization, and privacy preservation
GANs consist of a generator and discriminator network engaged in an adversarial training process, enabling the generation of highly realistic synthetic samples that resemble real patient data. In the medical domain, variants such as Conditional GANs (cGANs), CycleGANs, and Wasserstein GANs (WGANs) have demonstrated effectiveness in generating high-quality imaging data such as MRI, CT scans, and X-rays, as well as structured tabular clinical data
This paper provides a comprehensive review of GAN architectures applied to medical data, examines their strengths and limitations, and proposes a methodological framework for implementing GAN-based augmentation pipelines. The study also highlights challenges such as mode collapse, data privacy risks, and evaluation difficulties. Overall, GAN-based synthetic data generation presents a promising direction for enhancing AI-driven healthcare systems while addressing critical data availability constraints
