Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies
IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY, vol.6, pp.183-192, 2025 (ESCI, Scopus)
- Publication Type: Article / Article
- Volume: 6
- Publication Date: 2025
- Doi Number: 10.1109/ojemb.2024.3508472
- Journal Name: IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY
- Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, BIOSIS, Compendex, INSPEC, Directory of Open Access Journals
- Page Numbers: pp.183-192
- Istanbul University Affiliated: No
Abstract
Generative Adversarial Networks (GANs) have emerged as a powerful tool in artificial intelligence, particularly for unsupervised learning. This systematic review analyzes GAN applications in healthcare, focusing on image and signal-based studies across various clinical domains. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we reviewed 72 relevant journal articles. Our findings reveal that magnetic resonance imaging (MRI) and electrocardiogram (ECG) signal acquisition techniques were most utilized, with brain studies (22%), cardiology (18%), cancer (15%), ophthalmology (12%), and lung studies (10%) being the most researched areas. We discuss key GAN architectures, including cGAN (31%) and CycleGAN (18%), along with datasets, evaluation metrics, and performance outcomes. The review highlights promising data augmentation, anonymization, and multi-task learning results. We identify current limitations, such as the lack of standardized metrics and direct comparisons, and propose future directions, including the development of no-reference metrics, immersive simulation scenarios, and enhanced interpretability.