Evaluating the Utility of Synthetic Data in Medical Machine Learning Using Pairwise Correlation Distance

Introduction: The development of predictive models for medical applications is often hindered by the scarcity of high-quality datasets, particularly for rare diseases with limited documented cases. Incorporating synthetic data (SD) generated by advanced generative models (GMs) presents a promising solution [for full text, please go to the a.m. URL]