Leveraging Generative AI Models for Synthetic Data Generation in Healthcare: Balancing Research and Privacy

TL;DR

This paper explores how generative AI models like GANs and VAEs can create realistic synthetic healthcare data, balancing the need for research access with strict privacy regulations such as HIPAA and GDPR.

Contribution

It provides an analysis of generative AI techniques for synthetic healthcare data, discussing their benefits, challenges, and future research directions in privacy-preserving data sharing.

Findings

Synthetic data can effectively anonymize patient information.

Generative models enable versatile healthcare data applications.

Challenges include ensuring data realism and privacy compliance.

Abstract

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.