Synthetic Medical Data

Synthetic medical data generation aims to create realistic, privacy-preserving datasets for training and evaluating machine learning models in healthcare, overcoming limitations imposed by data scarcity and privacy regulations. Current research focuses on developing sophisticated generative models, including variational autoencoders, generative adversarial networks, and diffusion models, often incorporating large language models to enhance data realism and contextual richness, particularly for textual data like clinical notes. This field is crucial for advancing AI in healthcare, enabling the development of more robust and equitable diagnostic and prognostic tools while addressing ethical concerns around patient data privacy and algorithmic bias.