Deep Learning in Medical Applications: Challenges and Opportunities
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: 31 July 2025 | Viewed by 133
Special Issue Editors
Interests: bio-models; medical signal processing; nanoparticles imaging and therapy; deep brain stimulation; tele-medical systems; novel sensors on silicon for bio-medical applications; lab-on-a-chip for bio-makers
Interests: computer vision; computational histopathology, generative AI; foundation models; self supervised learning
Special Issue Information
Dear Colleagues,
Deep learning has revolutionized the field of medical imaging and data analysis, offering unprecedented accuracy and efficiency in diagnosing and treating various diseases. However, the integration of deep learning into medical applications is not without its challenges. One of the major hurdles is the need for large, high-quality datasets from sensors to train the algorithms effectively. Additionally, ensuring the reliability and interpretability of deep learning models in a clinical setting poses a significant challenge.
Despite these obstacles, the opportunities presented by deep learning in medicine are immense. By leveraging the power of artificial intelligence, healthcare providers can unlock new insights from complex medical data, leading to more personalized and effective treatments. Deep learning algorithms can also assist in early disease detection, improve patient outcomes, and streamline healthcare processes.
In this rapidly evolving field, researchers and practitioners are working tirelessly to overcome the challenges associated with deep learning for medical applications while harnessing its vast potential to transform the way we approach healthcare. Through collaboration and innovation, the future of medicine holds great promise with deep learning at its core.
Prof. Dr. Richard Bayford
Dr. Sikandar Ali
Dr. Ali Hussain
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- medical imaging
- healthcare
- artificial intelligence
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