Deep Supplement Learning for Healthcare and Biomedical Applications

Dear Colleagues,

During the challenging years of the ongoing COVID-19 pandemic, the need for efficient artificial intelligence models, tools, and applications in healthcare has been more evident than ever. Deep learning algorithms, supplemented by advanced learning techniques, have the potential to revolutionize healthcare and biomedical technologies. This Topic aims to bring together interdisciplinary approaches, focusing on innovative applications and existing AI methodologies to address pressing challenges in the healthcare sector. The topics of interest include deep learning algorithms in healthcare, supplemental learning techniques, biomedical data analysis, and medical image processing. These areas are crucial for interpreting genomic data, developing predictive models, and creating health monitoring systems. AI-driven drug discovery, personalized medicine, and the integration of deep learning with electronic health records (EHRs) are also key focal points. Moreover, this Topic will explore the development of clinical decision support systems, healthcare diagnostics, and AI-driven diagnosis in healthcare. The role of AI in health science education, rehabilitation, assistive technologies, public health, and ethical and legal considerations in AI healthcare applications will also be addressed. By highlighting the latest research, innovative approaches, and practical implementations, this Topic aims to improve patient outcomes, enhance diagnostic accuracy, and optimize treatment protocols. Researchers are encouraged to develop new or adapt existing AI models, tools, and applications to effectively solve the dynamic and heterogeneous nature of healthcare data problems. In addition to the open call for papers, extended versions of articles presented at relevant conferences are invited. Each submission should contain at least 50% new material, such as technical extensions, more in-depth evaluations, or additional use cases, to contribute significantly to the scientific literature in this field.

Prof. Dr. Tahir Cetin Akinci
Prof. Dr. Ömer Faruk Ertuğrul
Topic Editors