this systematic review explores the role of generative artificial intelligence (ai) in revolutionizing personalized medicine. the study highlights the challenges faced by precision medicine, including data collection, costs, and privacy issues. generative ai offers a promising solution by creating realistic, privacy-preserving patient data, which can enhance patient-centric healthcare. the review examines the application of deep generative models (dgms), particularly generative adversarial networks (gan), in clinical informatics, medical imaging, bioinformatics, and early diagnostics. the analysis focuses on the impact of dgms on precision medicine, with an emphasis on their ability to improve synthetic data generation, accuracy, and privacy. however, the study also identifies limitations, especially regarding the accuracy of foundation models like large language models (llms) in digital diagnostics. the conclusion emphasizes the importance of overcoming data scarcity and ensuring the generation of realistic, privacy-safe synthetic data to advance personalized medicine. further development of llms is crucial for improving diagnostic precision. the application of generative ai in personalized medicine is emerging, underscoring the need for more interdisciplinary research to advance this field. the review underscores the potential of generative ai to transform personalized medicine, while highlighting the need for continued research and development in this area.this systematic review explores the role of generative artificial intelligence (ai) in revolutionizing personalized medicine. the study highlights the challenges faced by precision medicine, including data collection, costs, and privacy issues. generative ai offers a promising solution by creating realistic, privacy-preserving patient data, which can enhance patient-centric healthcare. the review examines the application of deep generative models (dgms), particularly generative adversarial networks (gan), in clinical informatics, medical imaging, bioinformatics, and early diagnostics. the analysis focuses on the impact of dgms on precision medicine, with an emphasis on their ability to improve synthetic data generation, accuracy, and privacy. however, the study also identifies limitations, especially regarding the accuracy of foundation models like large language models (llms) in digital diagnostics. the conclusion emphasizes the importance of overcoming data scarcity and ensuring the generation of realistic, privacy-safe synthetic data to advance personalized medicine. further development of llms is crucial for improving diagnostic precision. the application of generative ai in personalized medicine is emerging, underscoring the need for more interdisciplinary research to advance this field. the review underscores the potential of generative ai to transform personalized medicine, while highlighting the need for continued research and development in this area.