Medical Data Sharing

Medical data sharing aims to leverage the power of large datasets for improved healthcare, while rigorously protecting patient privacy. Current research focuses on techniques like federated learning, dataset distillation, and the generation of synthetic data using large language models and diffusion models to enable collaborative research and AI development without directly sharing sensitive information. These methods are being evaluated across various medical imaging modalities and datasets, with a strong emphasis on balancing data utility with robust privacy preservation to ensure ethical and compliant data sharing practices. The ultimate goal is to facilitate advancements in diagnostics, treatment, and research while upholding patient confidentiality and data security.