Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generation.
Journal:
Computer methods and programs in biomedicine
PMID:
39742693
Abstract
BACKGROUND: Data sharing in healthcare is vital for advancing research and personalized medicine. However, the process is hindered by privacy, ethical, and legal challenges associated with patient data. Synthetic data generation emerges as a promising solution, replicating statistical properties of real data while enhancing privacy protection.