Anomaly detection is an essential part in medical field, the reason being it is best to identify a disease at its early stages so it can be cured or can be prevented from being worse. But sometimes it could be just a simple variation and not an actual disease, so it is important to distinguish between a disease and a variation. In medical field it is difficult to analyze high dimensional healthcare data along with finding the illness, ailment, etc. To overcome all these problems this paper focuses on a novel approach of using Generative Adversarial Networks (GAN) which can help improve the scope of anomaly detection in medical field. Here the GAN is trained on pre-existing datasets which helps in producing realistic images of the scan. To detect illnesses in their early stages, track patient health trends and avoid invalid diagnostic reports, this study aims to focus on a GAN-based anomaly detection method for a variety of medical data. To make the model reliable that can revolutionize medical diagnostics all issues related to training stability, data diversity and hyperparameters optimization are addressed.