Akpinar Muhammed Halil, Sengur Abdulkadir, Salvi Massimo, Seoni Silvia, Faust Oliver, Mir Hasan, Molinari Filippo, Acharya U Rajendra
Vocational School of Technical SciencesIstanbul University-Cerrahpasa 34320 Istanbul Türkiye.
Technology FacultyFirat University 23119 Elazig Türkiye.
IEEE Open J Eng Med Biol. 2024 Nov 28;6:183-192. doi: 10.1109/OJEMB.2024.3508472. eCollection 2025.
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.
生成对抗网络（GANs）已成为人工智能领域的一种强大工具，尤其适用于无监督学习。本系统综述分析了GAN在医疗保健领域的应用，重点关注跨多个临床领域基于图像和信号的研究。按照系统综述和Meta分析的首选报告项目（PRISMA）指南，我们检索了72篇相关期刊文章。我们的研究结果表明，磁共振成像（MRI）和心电图（ECG）信号采集技术应用最为广泛，脑部研究（22%）、心脏病学（18%）、癌症（15%）、眼科（12%）和肺部研究（10%）是研究最多的领域。我们讨论了关键的GAN架构，包括条件GAN（cGAN，31%）和循环GAN（CycleGAN，18%），以及数据集、评估指标和性能结果。该综述强调了有前景的数据增强、匿名化和多任务学习结果。我们指出了当前的局限性，如缺乏标准化指标和直接比较，并提出了未来的方向，包括开发无参考指标、沉浸式模拟场景以及增强可解释性。
