Year: 2024
Type: article
Abstract: The healthcare sector is increasingly reliant on data-driven methodologies to enhance patient outcomes, streamline operations, and drive research innovations. However, the sensitive nature of healthcare data, alongside stringent privacy regulations, poses significant barriers to the effective use and sharing of real patient data. Synthetic test data generation, particularly through Generative AI techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), presents a powerful solution. This paper explores the methodologies for creating synthetic healthcare data, emphasizing the advantages of these technologies in secured environments. Furthermore, it discusses various applications, challenges, ethical considerations, and future directions for synthetic data in healthcare, underscoring its potential to revolutionize the field while maintaining patient confidentiality and regulatory compliance.
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4404699571