Deep Learning for Medical Applications: Challenges and Opportunities

Dear Colleagues,

Rapid advancements in deep learning have revolutionized the field of medical applications, offering unprecedented opportunities to enhance healthcare delivery, diagnostics, and patient outcomes. From medical imaging and genomics to personalized medicine and real-time monitoring, deep learning has demonstrated its potential to transform traditional healthcare practices. However, integrating these sophisticated algorithms into clinical settings presents significant challenges, including data scarcity, model interpretability, ethical considerations, and privacy concerns.

This Special Issue, entitled "Deep Learning for Medical Applications: Challenges and Opportunities", provides a platform for the exploration of recent developments in the application of deep learning to medical domains. By bridging the gap between innovative AI research and practical medical applications, this Special Issue aims to advance the field of medical AI while enabling the discussion of obstacles and leveraging deep learning's full potential in healthcare.

Deep learning has revolutionized the field of medical research, offering unprecedented capabilities in areas such as diagnosis, prognosis, treatment planning, and disease monitoring. This Special Issue focuses on the transformative potential of deep learning techniques in medical applications, exploring both their immense opportunities and the challenges that remain in deploying these technologies in real-world clinical settings. We welcome the submission of high-quality, original contributions that address the following topics:

• The development and validation of deep learning models for medical image analysis (e.g., CT, MRI, X-ray, ultrasound).
• Health and medical behavior analytics with deep learning.
• Machine learning for image classification with CT/MRI/PET/X-ray/ultrasound.
• The application of deep learning in bioinformatics, genomics, and personalized medicine.
• The integration of multimodal data (e.g., imaging, electronic health records, and wearable device data) using deep learning.
• Approaches for improving model interpretability and explainability in medical contexts.
• Ethical and regulatory considerations in the use of AI-driven medical tools.
• Challenges in training deep learning models with limited, imbalanced, or noisy datasets.
• Advances in federated learning and privacy-preserving AI for healthcare applications.
• Multimodal medical image analysis.
• The deployment and scalability of deep learning models in clinical environments.

This Special Issue aims to present innovative research, share insights into practical implementation, and address critical gaps in the adoption of deep learning in healthcare. In this Special Issue, we welcome original research articles, reviews, and case studies that advance our understanding and application of deep learning in medical domains.

Dr. Roberta Hlavata
Dr. Robert Hudec
Dr. Patrik Kamencay
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• deep neural networks
• deep learning
• machine learning
• medical applications
• healthcare AI
• medical imaging
• multimodal data integration
• model interpretability