Revolutionizing Medical Data Generation: Evaluating the Performance of Generative AI Models in Producing Realistic and Diverse Clinical Data

Abstract

This paper explains how generative AI could be applied to create such synthetic digital health records, medical imaging data, and clinical trial data in medical data technology. The models used to generate such data are StyleGAN2 for images, CLIP for guidance, T5/ViT for specialized generation, and specialized tabular GANs. All of these were used to create data similar to that of the real world in medicine. Results show that generative AI alone can generate excellent quality medical data that is indistinguishable statistically from real-world data, where StyleGAN2 has high distribution fidelity with competitive results from CLIP and T5 for the same. This technology can fundamentally change medical data generation, content creation, and healthcare for better patient outcomes and productivity.

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