Healthcare Synthetic Data Generation using Generative Adversarial Networks

Abstract

Recently developed as powerful tools for synthetic data creation, Generative Adversarial Networks (GANs) show a lot of promise in healthcare applications, where data availability and privacy concerns are major issues. utilizing GANs for the generation of high-quality synthetic healthcare data, with the aim of protecting patient privacy while retaining important features and trends observed in actual data. There are data shortages, regulatory constraints, and privacy concerns in sensitive sectors such as medical imaging, patient records, and clinical research. GANs generate realistic, anonymized datasets, which eliminates these issues. We examine various GAN architectures and training methods with a focus on healthcare, assessing their ability to produce synthetic data for applications like as diagnosis, predictive modeling, and treatment planning. We also touch on some of the potential limitations and ethical issues with using GANs in healthcare, including issues with data accuracy and model interpretability. Greater access to healthcare data, enabled by GANs, has the potential to improve patient care, medical research, and AI-driven insights.