Generative AI for Medical Image Synthesis: GANs, Diffusion Models, and Data Augmentation Techniques

Generative models have emerged as a powerful paradigm for medical image synthesis and augmentation. The growing demand for fine-quality medical images has generated a significant amount of research attention, and these models are expected to address issues such as data sparsity, privacy concerns, and limited annotated datasets. We focus on the current state-of-the-art research works in deep learning techniques, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models, as well as their applications in synthesizing medical images. In this survey paper, we summarize the comparative review of efficient generative model architectures and review the development of controllable, multimodal, and realistic synthetic image generation methods. We also provide discussions on the current challenges and limitations, including limited data availability, overfitting, and regulatory frameworks. Moreover, the ethical considerations of using generative models in healthcare and their potential societal impacts are explored as well. We examine the recent literature, focus on impact research studies, and aim to inspire research fellows in exploring the most promising directions in synthetic medical imaging. At last, we outline future research opportunities that could help more effectively integrate generative models into clinical workflows and diagnostic systems, ultimately promoting more robust, explainable, and equitable AI in healthcare.