Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets

ArXi:2605.17758v1 Announce Type: new Synthetic data is widely used in healthcare to create datasets that are similar to original data but without the privacy concerns. Generating and evaluating synthetic data across privacy, utility and fairness is crucial for facilitating high quality data availability for downstream prediction tasks and clinical decision making. We present Memisis, a tool that orchestrates and evaluates synthetic data by leveraging existing synthetic data tools, the power of large language models and state-of-the-art evaluation metrics.