Generative Artificial Intelligence (AI) is rapidly changing the healthcare field by allowing new solutions in diagnostics, treatment planning, drug discovery, medical imaging, and patient care. This survey reviews the latest advancements in generative AI models, especially deep generative frameworks like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and large language models (LLMs), and their uses in healthcare. We look at how these models improve diagnostic accuracy by creating realistic medical images, support personalized medicine through predictive modeling, and speed up drug design by generating candidate compounds. We also discuss key challenges like data privacy, model interpretability, ethical issues, and regulatory hurdles to provide a well-rounded view. The survey highlights emerging trends such as multimodal generative systems, federated generative learning, and integration with electronic health records (EHRs), showing their potential to improve clinical results. Finally, we suggest future directions for research and deployment, stressing the need for strong evaluation frameworks, collaboration across fields, and responsible AI practices. This work aims to give researchers, clinicians, and policymakers a clear understanding of current capabilities and future developments at the intersection of generative AI and healthcare. In addition to clinical and research uses, generative AI is transforming healthcare operations and medical education. Hospitals and healthcare systems are looking into generative models to improve resource allocation, predict patient admissions, and simulate emergency response scenarios. AI-generated virtual patients are used in medical training to create realistic case studies. This allows students and professionals to practice diagnostic reasoning in safe, controlled environments.
Shibin et al. (Fri,) studied this question.