Leveraging Generative AI Models for Synthetic Data Generation in Healthcare: Balancing Research and Privacy
This addresses privacy issues for healthcare researchers and practitioners, but it is incremental as it builds on existing generative AI methods.
The paper tackles the problem of privacy and regulatory challenges in using real patient data for healthcare research by proposing synthetic data generation with generative AI models like GANs and VAEs, aiming to provide anonymized data to enhance patient outcomes and diagnostics.
The widespread adoption of electronic health records and digital healthcare data has created a demand for data-driven insights to enhance patient outcomes, diagnostics, and treatments. However, using real patient data presents privacy and regulatory challenges, including compliance with HIPAA and GDPR. Synthetic data generation, using generative AI models like GANs and VAEs offers a promising solution to balance valuable data access and patient privacy protection. In this paper, we examine generative AI models for creating realistic, anonymized patient data for research and training, explore synthetic data applications in healthcare, and discuss its benefits, challenges, and future research directions. Synthetic data has the potential to revolutionize healthcare by providing anonymized patient data while preserving privacy and enabling versatile applications.