Generative Adversarial Networks in Dermatology: A Narrative Review of Current Applications, Challenges, and Future Perspectives
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| Veröffentlicht in: | Bioengineering vol. 12, no. 10 (2025), p. 1113-1144 |
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| Online-Zugang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3265832627 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2306-5354 | ||
| 024 | 7 | |a 10.3390/bioengineering12101113 |2 doi | |
| 035 | |a 3265832627 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Izu-Belloso Rosa Maria |u Medicine Faculty, Hospital Universitario Basurto, 48013 Bilbao, Spain | |
| 245 | 1 | |a Generative Adversarial Networks in Dermatology: A Narrative Review of Current Applications, Challenges, and Future Perspectives | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Literature Review | ||
| 520 | 3 | |a Generative Adversarial Networks (GANs) have emerged as powerful tools in artificial intelligence (AI) with growing relevance in medical imaging. In dermatology, GANs are revolutionizing image analysis, enabling synthetic image generation, data augmentation, color standardization, and improved diagnostic model training. This narrative review explores the landscape of GAN applications in dermatology, systematically analyzing 27 key studies and identifying 11 main clinical use cases. These range from the synthesis of under-represented skin phenotypes to segmentation, denoising, and super-resolution imaging. The review also examines the commercial implementations of GAN-based solutions relevant to practicing dermatologists. We present a comparative summary of GAN architectures, including DCGAN, cGAN, StyleGAN, CycleGAN, and advanced hybrids. We analyze technical metrics used to evaluate performance—such as Fréchet Inception Distance (FID), SSIM, Inception Score, and Dice Coefficient—and discuss challenges like data imbalance, overfitting, and the lack of clinical validation. Additionally, we review ethical concerns and regulatory limitations. Our findings highlight the transformative potential of GANs in dermatology while emphasizing the need for standardized protocols and rigorous validation. While early results are promising, few models have yet reached real-world clinical integration. The democratization of AI tools and open-access datasets are pivotal to ensure equitable dermatologic care across diverse populations. This review serves as a comprehensive resource for dermatologists, researchers, and developers interested in applying GANs in dermatological practice and research. Future directions include multimodal integration, clinical trials, and explainable GANs to facilitate adoption in daily clinical workflows. | |
| 610 | 4 | |a National Library of Medicine | |
| 651 | 4 | |a United States--US | |
| 653 | |a Dermatology | ||
| 653 | |a Clinical trials | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Deep learning | ||
| 653 | |a Medical research | ||
| 653 | |a Image resolution | ||
| 653 | |a Trends | ||
| 653 | |a Phenotypes | ||
| 653 | |a Medical imaging | ||
| 653 | |a Skin diseases | ||
| 653 | |a Generative adversarial networks | ||
| 653 | |a Image processing | ||
| 653 | |a Hybrids | ||
| 653 | |a Data augmentation | ||
| 653 | |a Image analysis | ||
| 653 | |a Medicine | ||
| 653 | |a Standardization | ||
| 653 | |a National libraries | ||
| 653 | |a Image segmentation | ||
| 653 | |a Neural networks | ||
| 653 | |a Classification | ||
| 653 | |a Literature reviews | ||
| 653 | |a Sensory integration | ||
| 653 | |a Synthetic data | ||
| 700 | 1 | |a Ibarrola-Altuna Rafael |u Hospital Universitario Galdakao, 48960 Bizkaia, Spain; rafael.ibarrolaaltuna@osakidetza.eus | |
| 700 | 1 | |a Rodriguez-Alonso, Alex |u Facultad de Medicina, Universidad del Pais Vasco/EHU, 48940 Leioa, Spain; alecai.rodriguez@gmail.com | |
| 773 | 0 | |t Bioengineering |g vol. 12, no. 10 (2025), p. 1113-1144 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3265832627/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3265832627/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3265832627/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |