With the advent of Electronic Health Records (EHR), healthcare systems have benefited significantly from improved patient care and enhanced data management. In order to advance research and education, patients’ medical records need to be shared across different departments, institutions, or even countries. However, as medical data is considered as highly sensitive information that can be easily exploited for malicious activities, protecting patients’ privacy within the system is essential. The more data researchers have access to, the higher the quality of the results of their projects will be. In that regard, there is a tremendous need of data for various projects, education, prototyping, and prediction. There are certain strict regulations regarding handling EHRs in different parts of the world, such as Health Insurance Portability and Accountability Act (HIPAA) in the United States and General Data Protection Regulation (GDPR) in the European union. These rules ensure the privacy and safety of patients by safeguarding their information and restrict sharing and using their personal information. Therefore, providing vast amounts of data for training neural networks or any other possible application can be challenging. Synthetic Data Generation (SDG) can be a solution to the ethical issues, by providing similar fabricated data to be utilized in different situations without compromising patient confidentiality. Generative Artificial Intelligence (GenAI) models have exceptional abilities in generating different types of data including text, image, or audio. These models excel at capturing the patterns and statistical features of the original data to create synthetic samples that highly resemble the real data. In this chapter, we will discuss SDG solutions in healthcare and review existing methods and techniques used in the field, as well as the associated challenges and risks.