About this Research Topic
Generative Adversarial Networks (GANs) have garnered attention across various domains for their ability to generate realistic data, with remarkable success in image generation, data synthesis, and unsupervised learning tasks. In the context of biomedical informatics, GANs have been applied to generate synthetic medical images, improving diagnostic accuracy, and addressing class imbalance in medical datasets. Given the rapid growth of medical data, GANs present a promising avenue for enhancing predictive analytics, medical image processing, and drug discovery.
The aim of this Research Topic is to explore and highlight recent advancements and applications of GANs in biomedical informatics. It will cover the various ways GANs have been applied to improve healthcare outcomes, including their role in medical imaging, disease prediction, drug development, and personalized medicine. The topic will address current challenges, such as data scarcity, and discuss potential solutions, making this technology more accessible and effective in real-world medical scenarios.
We invite authors to submit original research articles, reviews, and case studies that explore:
◦ The use of GANs in medical image generation (MRI, CT, X-ray, etc.).
◦ Applications of GANs for enhancing diagnostic tools and predictive models.
◦ Addressing data imbalance and privacy concerns in biomedical datasets using GANs.
◦ Drug discovery and molecular synthesis applications powered by GANs.
◦ The integration of GANs with other AI techniques to improve healthcare delivery and patient outcomes.
◦ Challenges, limitations, and future directions in applying GANs to biomedical informatics.
Authors are encouraged to discuss both theoretical advancements and practical applications in the field.
The aim of this Research Topic is to explore and highlight recent advancements and applications of GANs in biomedical informatics. It will cover the various ways GANs have been applied to improve healthcare outcomes, including their role in medical imaging, disease prediction, drug development, and personalized medicine. The topic will address current challenges, such as data scarcity, and discuss potential solutions, making this technology more accessible and effective in real-world medical scenarios.
We invite authors to submit original research articles, reviews, and case studies that explore:
◦ The use of GANs in medical image generation (MRI, CT, X-ray, etc.).
◦ Applications of GANs for enhancing diagnostic tools and predictive models.
◦ Addressing data imbalance and privacy concerns in biomedical datasets using GANs.
◦ Drug discovery and molecular synthesis applications powered by GANs.
◦ The integration of GANs with other AI techniques to improve healthcare delivery and patient outcomes.
◦ Challenges, limitations, and future directions in applying GANs to biomedical informatics.
Authors are encouraged to discuss both theoretical advancements and practical applications in the field.
Keywords: Generative Adversarial Networks (GANs), Biomedical Informatics, Medical Image Generation, Disease Prediction, Drug Discovery, Healthcare AI Applications
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
