AI has become a part and parcel of the medical industry. Especially machine learning-based deep learning models with their ability to handle large amount of data continuously made significant contributions to the healthcare industry. One of the most promising technological developments in the field of deep learning is “deepfake”. Deepfake is a method of synthesizing fake images, audio, or video through deep learning models. Deepfake medical image processing is used in different applications like medical image synthesizing, modality transfer, data augmentation, dataset expansion, resolution enhancement, denoising, and reconstruction of medical images. Therefore, its wide range of applications provided a new realm of opportunity for the medical professional for quick, easy, and accurate diagnosis of dis-ease. This study provides an overview of deepfake implementation in the field of medical image processing. This paper is focused on providing clarity for the research questions (RQs), What are deepfake? What are the most common methods to generate a medical deepfake? What are the major applications of deepfake in the medical field? What is the need for deepfake detection? These research questions may help the researchers and academicians to understand the domain specified.