A Survey of Generative Adversarial Networks for Medical Images

Sameera V. Mohd Sagheer^1,#,*, U. Nimitha^2,#, P. M. Ameer², Muneer Parayangat³, Mohamed Abbas³, Krishna Prakash Arunachalam⁴
1 Department of Biomedical Engineering, KMCT College of Engineering for Women, Kozhikode, 673601, Kerala, India
2 Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kozhikode, 673601, Kerala, India
3 Electrical Engineering Department, College of Engineering, King Khalid University, Abha, 61413, Saudi Arabia
4 Departamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica Metropolitana, Santiago, 7800002, Chile
* Corresponding Author: Sameera V. Mohd Sagheer. Email: email
# These authors contributed equally to this work
(This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.067108

Received 25 April 2025; Accepted 08 August 2025; Published online 29 January 2026

Abstract

Over the years, Generative Adversarial Networks (GANs) have revolutionized the medical imaging industry for applications such as image synthesis, denoising, super resolution, data augmentation, and cross-modality translation. The objective of this review is to evaluate the advances, relevances, and limitations of GANs in medical imaging. An organised literature review was conducted following the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed, IEEE Xplore, and Scopus. The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review. Thesis, white papers, communication letters, and non-English articles were not included for the same. CLAIM based quality assessment criteria were applied to the included studies to assess the quality. The study classifies diverse GAN architectures, summarizing their clinical applications, technical performances, and their implementation hardships. Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy, reducing data scarcity through synthetic data generation, and supporting modality translation. However, concerns such as limited generalizability, lack of clinical validation, and regulatory constraints persist. This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions. Though GANs hold transformative capability for medical imaging, their integration into clinical use demands further validation, interpretability, and regulatory alignment.