With the rapid development of medical informatization and artificial intelligence technology, medical data, as a core resource, faces dual challenges of privacy protection and efficient utilization that urgently need to be addressed. Traditional privacy protection methods often sacrifice data utility and are difficult to meet the needs of complex medical scenarios. Generative Adversarial Networks (GANs), as a powerful generative model, can generate high-quality synthetic data while protecting privacy. Therefore, this article proposes a medical data privacy protection method based on Generative Adversarial Networks (GANs). By constructing an improved GANs model, it generates synthetic medical data that not only protects privacy but also has high practicality, effectively improving the practicality of synthetic data and providing a new solution for the secure sharing and application of medical data.

Zhiqiong Zou. Research on Medical Data Privacy Protection and Synthetic Data Generation Based on Generative Adversarial Networks. International Journal of Health and Pharmaceutical Medicine (2025), Vol. 5, Issue 1: 22-31. https://doi.org/10.38007/IJHPM.2025.050103.

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