Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies

Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies

Blog Article

Generative Adversarial Networks (GANs) have emerged as a powerful tool in artificial intelligence, fine mic 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 astros big chain necklace direct comparisons, and propose future directions, including the development of no-reference metrics, immersive simulation scenarios, and enhanced interpretability.