This systematic review explores the role of deep generative models (DGMs), particularly Generative Adversarial Networks (GANs), in advancing precision medicine. The study, conducted according to PRISMA guidelines, analyzes research from databases such as Scopus and PubMed, focusing on the impact of AI in precision medicine and DGMs' applications in synthetic data generation. The review highlights that DGMs, especially GANs, have improved the accuracy and privacy of synthetic data, but also identifies limitations, particularly in the accuracy of foundation models like Large Language Models (LLMs) in digital diagnostics. The conclusion emphasizes the importance of overcoming data scarcity and ensuring realistic, privacy-safe synthetic data generation to advance personalized medicine. Further development of LLMs is crucial for improving diagnostic precision, and interdisciplinary research is needed to fully realize the potential of generative AI in this field.This systematic review explores the role of deep generative models (DGMs), particularly Generative Adversarial Networks (GANs), in advancing precision medicine. The study, conducted according to PRISMA guidelines, analyzes research from databases such as Scopus and PubMed, focusing on the impact of AI in precision medicine and DGMs' applications in synthetic data generation. The review highlights that DGMs, especially GANs, have improved the accuracy and privacy of synthetic data, but also identifies limitations, particularly in the accuracy of foundation models like Large Language Models (LLMs) in digital diagnostics. The conclusion emphasizes the importance of overcoming data scarcity and ensuring realistic, privacy-safe synthetic data generation to advance personalized medicine. Further development of LLMs is crucial for improving diagnostic precision, and interdisciplinary research is needed to fully realize the potential of generative AI in this field.