Generative Adversarial Networks (GANs) for Synthetic Data Generation in Healthcare Research

Abstract

In 2023, the use of Generative Adversarial Networks (GANs) revolutionized data availability in healthcare research. This study explores the application of GANs to generate high-fidelity synthetic healthcare data, addressing privacy concerns and data scarcity issues. The synthetic data, derived from real patient records, retained the statistical properties and correlations of the original datasets, making it suitable for training and validating AI models. The study highlights the potential of GANs in expanding access to large, diverse datasets for healthcare AI research, enabling more robust model development while preserving patient privacy.