A Systematic Review on Synthetic Medical Images Generation-Recent Trends and Future Opportunities

Background/Objectives: Generative Models have revolutionized the synthesis of complex, realistic medical images. However, obtaining annotated, high-quality datasets is challenging and expensive due to privacy concerns, high human annotation costs, and data scarcity. This systematic literature review (SLR) provides a comparative overview of Generative AI models used for medical image generation, categorizing the research into GANs, Diffusion Models, and Autoencoders used for medical image generation and GAN loss functions. Methods: This systematic review followed the PRISMA 2020 guidelines. Studies published between 2021 and 2026 were retrieved from selected scientific databases and screened using predefined inclusion criteria. We selected 120 papers published in reputable databases and journals. The review examined generative modeling approaches aimed at addressing data scarcity and annotation limitations in medical imaging, including GAN-based adversarial synthesis, diffusion-based iterative denoising, and Autoencoder-based latent representation learning, along with an in-depth analysis of GAN loss functions. Results: GANs were the most widely used approach (36% of the reviewed studies), achieving the lowest FID scores (15.552-31.349). Diffusion Models (26% of the reviewed studies) showed superior structural fidelity, achieving SSIM values of up to 0.915, whereas GANs achieved 0.593 on brain MRI datasets. In addition, hybrid loss functions combining perceptual, structural, and pixel-level terms resulted in improved image quality performance (PSNR = 35.6; SSIM = 0.95). Conclusions: The analysis showed that GAN-based generative models produce visually realistic images with detailed textures, whereas Diffusion Models generate high-resolution images with superior structural fidelity but require substantial computational resources for training and image generation. Furthermore, the clinical implications, limitations, and challenges of Generative AI models were discussed. Despite these advances, the medical imaging field still faces several challenges, including the need for annotated medical datasets, high-quality realistic images for training, and limited data availability.