The healthcare field is on the cusp of a data revolution, thanks to the rise of generative AI. This technology, explored in a new systematic review, promises to address a critical challenge: the scarcity of diverse, high-quality medical data. Traditionally, medical AI development has been hampered by limited access to patient data due to privacy regulations and data sharing restrictions. Generative AI offers a powerful solution by creating synthetic data that mimics real patient information across various formats like medical images (X-rays, CT scans, MRIs), electronic health records, time-series physiological signals (ECGs, EEGs), and even clinical text notes. This opens up exciting possibilities for training more robust and accurate AI models without compromising patient privacy. The review highlights the potential of generative models like GANs, VAEs, diffusion models, and large language models to create highly realistic synthetic data that captures the complex variations and patterns found in real medical datasets. This synthetic data can then be used to augment limited datasets, train AI algorithms for tasks like disease diagnosis and treatment planning, and even help evaluate the performance and fairness of these algorithms. While the potential of generative AI for synthetic medical data is immense, challenges remain. One key area is the need for standardized evaluation metrics to ensure the quality, realism, and privacy of the synthetic data. The review emphasizes the importance of developing robust evaluation frameworks that go beyond generic image quality metrics and consider the specific clinical requirements of each data modality. Another challenge lies in effectively incorporating prior clinical knowledge and patient context into the data generation process. Current models often struggle to fully capture the intricate relationships between patient characteristics, disease progression, and treatment outcomes. Future research should focus on developing more personalized synthetic data that accurately reflects the diversity and complexity of real-world patient populations. The use of generative AI for synthetic data generation holds enormous promise for accelerating the development and deployment of medical AI, ultimately leading to better patient care and improved clinical outcomes.