Advances in AI: Employing Deep Generative Models for the Creation of Synthetic Healthcare Datasets to Improve Predictive Analytics

In the rapidly evolving domain of healthcare, the availability of large and diverse datasets is paramount for the development and validation of advanced algorithms. However, the acquisition of such data is often hindered by privacy concerns, limited patient cohorts, and the inherent variability in medical conditions. This research delves into the potential of Deep Generative Models (DGMs) as a solution for synthetic data generation in healthcare applications. We present a comprehensive study of various DGM architectures, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Normalizing Flows, and their efficacy in generating high-fidelity medical images. Our experiments demonstrate that these models can produce synthetic data that retains the intricate features of real medical images while ensuring patient privacy. Furthermore, when used as augmentation in training diagnostic algorithms, the synthetic data generated by our proposed models showed a significant improvement in the generalization capabilities of these algorithms. This work not only underscores the potential of DGMs in addressing the data scarcity issue in healthcare but also paves the way for their broader application in medical research, training, and diagnostics.