In my talk, I will showcase how synthetic data, generated by deep generative models based on real-world data, enables solutions in healthcare that are unattainable with real data alone. I will discuss the transformation of biased datasets into unbiased ones using synthetic data. My talk will also explore how generative models facilitate transfer learning across various domains, enhancing the versatility of machine learning models. I will also cover the importance of data augmentation, where synthetic data enriches training sets for more comprehensive machine learning outcomes. Additionally, I will highlight the crucial role of synthetic data in the thorough testing and debugging of these models, ensuring their dependability in healthcare settings.