Generative Artificial Intelligence (AI) Healthcare System has a game exchange that opens new ways for innovation in diagnostics, drug discovery, chip therapy, and patient engagement. This semantic review examines several applications of generative AI. Its basic methods, such as generative adversarial networks (GAN), variational autoencoders, and diffusion models, will transform clinical practice and research. Using synthetic data, increased medical imaging can improve future analysis and promote well-organized administrative procedures, generative AI patient results, low costs, and health efficiency. Reviews have also noticed important moral, legal, and regulatory concerns, including algorithm bias, privacy, and responsible distribution. Through intensive analysis of recent research and study of the case in the real world, it emphasizes the importance of multi-anchored collaboration and implementation science to adapt the ability of generic AI in paper health services.