cirpublications.com-6Y1r-1790874880-j314922670 1790874880 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2026 5 2 Kwame Mensah Kojo Asante Nana Boateng 2026 10.68159/j314922670 https://cirpublications.com/pub/journal/1/article/j314922670 Nie D, Trullo R, Lian J, Petitjean C, Ruan S, Wang Q, et al. Medical Image Synthesis with Context-Aware Generative Adversarial Networks. Med Image Comput Comput Assist Interv. 2017;10435:417-25. Wolterink JM, Leiner T, Viergever MA, Išgum I. Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging. 2017;36(12):2536-45. 10.1109/TMI.2017.2708987 Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: A review. Med Image Anal. 2019;58:101552. 10.1016/j.media.2019.101552 Koetzier LR, Wu J, Mastrodicasa D, Lutz A, Chung M, Koszek WA, et al. Generating Synthetic Data for Medical Imaging. Radiology. 2024;312(3):e232471. 10.1148/radiol.232471 Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H, et al. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing. 2018;321:321-31. 10.1016/j.neucom.2018.09.013 Dar SU, Yurt M, Karacan L, Erdem A, Erdem E, Cukur T. Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks. IEEE Trans Med Imaging. 2019;38(10):2375-88. 10.1109/TMI.2019.2901750 Yang H, Sun J, Carass A, Zhao C, Lee J, Prince JL, et al. Unsupervised MR-to-CT Synthesis Using Structure-Constrained CycleGAN. IEEE Trans Med Imaging. 2020;39(12):4249-61. 10.1109/TMI.2020.3002932 Li F, Huang W, Luo M, Zhang P, Zha Y. A new VAE-GAN model to synthesize arterial spin labeling images from structural MRI. Displays. 2021;70:102079. 10.1016/j.displa.2021.102079 Pan S, Wang T, Qiu RLJ, Axente M, Chang CW, Peng J, et al. 2D medical image synthesis using transformer-based denoising diffusion probabilistic model. Phys Med Biol. 2023;68(10):105004. Kazerouni A, Aghdam EK, Heidari M, Azad R, Fayyaz M, Hacihaliloglu I, et al. Diffusion models in medical imaging: A comprehensive survey. Med Image Anal. 2023;88:102846. 10.1016/j.media.2023.102846 Khader F, Müller-Franzes G, Tayebi Arasteh S, Han T, Haarburger C, Schulze-Hagen M, et al. Denoising diffusion probabilistic models for 3D medical image generation. Sci Rep. 2023;13(1):7303. 10.1038/s41598-023-34341-2 Chen Y, Yang XH, Wei Z, Heidari AA, Zheng N, Li Z, et al. Generative Adversarial Networks in Medical Image augmentation: A review. Comput Biol Med. 2022;144:105382. 10.1016/j.compbiomed.2022.105382 Xue Y, Ye J, Zhou Q, Long LR, Antani S, Xue Z, et al. Selective synthetic augmentation with HistoGAN for improved histopathology image classification. Med Image Anal. 2021;67:101816. 10.1016/j.media.2020.101816 Li W, Li J, Polson J, Wang Z, Speier W, Arnold C. High resolution histopathology image generation and segmentation through adversarial training. Med Image Anal. 2022 ;75:102251. 10.1016/j.media.2021.102251 Dayarathna S, Islam KT, Uribe S, Yang G, Hayat M, Chen Z. Deep learning based synthesis of MRI, CT and PET: Review and analysis. Med Image Anal. 2024;92:103046. 10.1016/j.media.2024.103046 Fan Y, Liao H, Huang S, Luo Y, Fu H, Qi H, et al. A survey of emerging applications of diffusion probabilistic models in MRI. Meta-Radiology. 2024;2(2):100082. 10.1016/j.metrad.2024.100082 Lei Y, Harms J, Wang T, Liu Y, Shu HK, Jani AB, et al. MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks. Med Phys. 2019;46(8):3565-81. 10.1002/mp.13617 Kearney V, Ziemer BP, Perry A, Wang T, Chan JW, Ma L, et al. Attention-Aware Discrimination for MR-to-CT Image Translation Using Cycle-Consistent Generative Adversarial Networks. Radiol Artif Intell. 2020;2(2):e190027. 10.1148/ryai.2020190027 Huijben EM, Pluim JP, van Eijnatten MA. Denoising diffusion probabilistic models for addressing data limitations in chest X-ray classification. Inform Med Unlocked. 2024;50:101575. 10.1016/j.imu.2024.101575 Wolterink JM, Dinkla AM, Savenije MH, Seevinck PR, van den Berg CA, Isgum I, et al. Deep MR to CT synthesis using unpaired data. In: Int Workshop Simul Synth Med Imaging. Cham: Springer; 2017. p. 14-23. Azad M, Fahad NM, Raiaan MAK, Anik TR, Khan MFK, Toyé HMK, et al. A Systematic Review of Diffusion Models for Medical Image-Based Diagnosis: Methods, Taxonomies, Clinical Integration, Explainability, and Future Directions. Diagnostics (Basel). 2026;16(2):211. 10.3390/diagnostics16020211 Qi M, Li Y, Wu A, Jia Q, Li B, Sun W, et al. Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy. Med Phys. 2020;47(4):1880-94. 10.1002/mp.14075 Hetz MJ, Bucher TC, Brinker TJ. Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images. Med Image Anal. 2024;94:103149. 10.1016/j.media.2024.103149 Cackowski S, Barbier EL, Dojat M, Christen T. ImUnity: A generalizable VAE-GAN solution for multicenter MR image harmonization. Med Image Anal. 2023;88:102799. 10.1016/j.media.2023.102799 Kui X, Liu B, Sun Z, Li Q, Zhang M, Liang W, et al. Med-LVDM: Medical latent variational diffusion model for medical image translation. Biomed Signal Process Control. 2025;106:107735. 10.1016/j.bspc.2025.107735 Sharma A, Hamarneh G. Missing MRI pulse sequence synthesis using multi-modal generative adversarial network. IEEE Trans Med Imaging. 2020;39(4):1170-83. 10.1109/TMI.2019.2945523 Xiang L, Wang Q, Nie D, Zhang L, Jin X, Qiao Y, et al. Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image. Med Image Anal. 2018;47:31-44. 10.1016/j.media.2018.03.013 Alajaji SA, Khoury ZH, Elgharib M, Saeed M, Ahmed ARH, Khan MB, et al. Generative Adversarial Networks in Digital Histopathology: Current Applications, Limitations, Ethical Considerations, and Future Directions. Mod Pathol. 2024;37(1):100369. 10.1016/j.modpat.2023.100369 Qin Z, Liu Z, Zhu P, Xue Y. A GAN-based image synthesis method for skin lesion classification. Comput Methods Programs Biomed. 2020;195:105568. 10.1016/j.cmpb.2020.105568