The role of generative AI in medical image synthesis: A review

Abstract

Medical imaging is transformed with Generative artificial intelligence (AI) that offers robust tools for image synthesis, data augmentation and enhancement of image quality. Generative Adversarial Networks (GANs) have established themselves among the various generative models as particularly robust in synthesizing realistic medical images near real-world clinical data. This review discusses the growing importance of generative AI in synthesizing medical images, its use in applications like radiology, pathology and other medical disciplines. We present the overview of some of the significant generative models, e.g., Variational Autoencoders (VAEs) and Diffusion Models and their advantages, disadvantages and prospects. We also present the challenges that accompany such models like interpretability, transferability to other medical disciplines and the ethics of applying synthetic data to real-world clinical practice. Further, the review presents recent developments in hybrid AI approaches that combine AI and physics-based models along with multimodal learning in an attempt to enhance the trustworthiness and accuracy of generative methods. Finally, we look forward to future research directions like federated learning and explainable AI (XAI) that will enable the safe and successful application of generative AI in medicine. XAI methods—such as visual attribution (e.g., Grad-CAM, SHAP), latent space interpretability in VAEs, and external symbolic explanation frameworks—are particularly important in improving trust and understanding in clinical applications [Huff et al., 2021; Bhati et al., 2024]. The aim of this paper is to facilitate researchers and practitioners to showcase the full potential of generative AI for medical imaging by presenting a comprehensive review of existing techniques and emerging trends.

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