Synthetic Health Data

Synthetic health data aims to create realistic, privacy-preserving datasets that mirror the statistical properties of real patient information, enabling research and development without compromising confidentiality. Current research focuses on improving the accuracy and fairness of synthetic data generation, employing techniques like Generative Adversarial Networks (GANs) and diffusion models to handle diverse data types and mitigate biases in downstream analyses. This approach facilitates broader data sharing for advancements in AI-driven healthcare applications, such as developing clinical decision support systems and improving predictive models, while addressing ethical and regulatory concerns surrounding sensitive patient data.