The generation of medical research requires patient data which faces barriers because of strict privacy rules and limited access possibilities. GANs demonstrate potential as a solution for generating artificial healthcare data that maintains statistical integrity while protecting privacy according to Goslinski et al. [1] and Choi et al. [2]. The document explores how different GAN architectures perform in generating medical documents that look authentic. Different GAN models produce synthetic medical data that undergoes statistical testing for feature analysis and distribution evaluation. To determine the level of realism alongside privacy protection between synthetic data and patient data, researchers executed a comparison assessment. The research indicates that GANs provide an effective answer to medical research data deficiency by generating healthcare data patterns while maintaining patient confidentiality. GAN-generated synthetic data provides accessible healthcare data that meets privacy requirements to advance healthcare analytics research.