Generative AI is revolutionizing the healthcare industry by enabling innovative solutions for diagnostics, treatment planning, drug discovery, and patient care. One of the most significant advances in healthcare has been the development of generative models. These models, such as generative adversarial networks (GANs), variational autoencoders (VAEs) and diffusion models (DMs), have enabled healthcare professionals to develop innovative solutions more effectively and efficiently. GenAI models assist in tasks such as enhancing diagnostic accuracy, accelerating drug discovery, and supporting clinical decision-making processes by simulating complex medical phenomena and generating realistic datasets for training and validation. Despite its potential for innovation, there are challenges to the integration of GenAI into healthcare systems, including ensuring data security, addressing ethical concerns, and maintaining regulatory compliance. This study examines the role of GenAI in modern healthcare, focusing on its current applications, potential benefits and associated challenges.