Generative Adversarial Networks (GANs) are powerful AI models used to generate synthetic medical images. Due to the limited availability of high-quality medical imaging datasets, training deep learning models can be inefficient. This proof-of-concept explores how GAN-generated Echocardiograms and CMR images can be used to supplement real-world data, potentially improving model performance in tasks such as image segmentation and disease detection.