Synthetic data utilization for model training in healthcare has grown in the last 15 years. However, there are several risks associated with this approach such as, privacy issues, where the patient’s data can be identifiable; data quality, where the complexity of the real-world scenarios is not captured; and ethical concerns, concerning malicious misuse. This paper highlights the challenges of using synthetic data by outlining the risks to contribute to developing more reliable, fair, and solutions.