Generative AI Approaches for Synthetic Health Data Generation
Abstract
Generating realistic synthetic health data using Artificial Intelligence models offers a powerful solution to privacy restrictions and data scarcity in healthcare. Modern generative frameworks such as Generative Adversarial Networks, Variational Autoencoders, diffusion models, and transformer-based language models enable the synthesis of diverse clinical data, including structured EHR tables, time-series vitals, unstructured clinical notes, and medical images. These models are evaluated on fidelity, utility, and privacy, with applications ranging from data augmentation to privacy-preserving data sharing. GANs excel at learning joint distributions, VAEs capture smooth latent structures, diffusion models yield high-fidelity samples, and transformers produce coherent medical text. Challenges remain in ensuring clinical validity, diversity, and robust privacy protection while avoiding biases and mode collapse. Emerging research explores multimodal generation, promising richer, more integrated synthetic patient records that can transform healthcare research and AI model development.
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