About this Research Topic
The growth of digital cities necessitates the efficient management of energy systems, transportation networks, information, and healthcare networks through the use of big data. The deployment of distributed nodes such as sensors, actuators, processors, and communication devices poses challenges for social services. A crucial aspect of smart cities is the utilization of advanced technology for remote medical care, including remote surgical procedures, wearable sensors for disease prediction, and AI-powered medical assistants. This requires the use of high-performance computing and advanced intelligence for healthcare networks through the analysis of big data. Utilizing deep learning techniques such as transfer learning has become increasingly important for edge/fog bio-computing nodes and the internet of health things. With new developments in deep learning such as fusion-based and blind techniques for the internet of health things applications emerging, research on the implementation of deep learning in telehealth settings is an area of growing interest.
The proposed Research Topic will investigate the intersection of artificial intelligence, health optimization, and the internet of medical things, with a focus on the utilization of big data-driven deep learning in medical data analytics. The investigation will specifically address the challenges that arise from the integration of advanced AI and IoT health concepts in regard to secure and encrypted big data processing and analysis. We are seeking submissions of high-quality research and cutting-edge surveys that will provide readers with the most up-to-date information on innovative big data and deep learning tools in the healthcare field.
• The application of big data analytics in the healthcare industry
• The challenges of secure and encrypted big data processing and analysis in healthcare
• Innovative big data and deep learning tools for improving healthcare intelligence
• Utilizing deep learning for biomedical service delivery, such as remote medical care and telehealth
• The use of big data and deep learning in edge/fog bio-computing nodes and the internet of health things
• The impact of big data and deep learning on healthcare service delivery and patient outcomes
• The ethical and privacy considerations of utilizing big data and deep learning in healthcare
• The future of big data and deep learning in healthcare and biomedical service delivery
The proposed Research Topic will investigate the intersection of artificial intelligence, health optimization, and the internet of medical things, with a focus on the utilization of big data-driven deep learning in medical data analytics. The investigation will specifically address the challenges that arise from the integration of advanced AI and IoT health concepts in regard to secure and encrypted big data processing and analysis. We are seeking submissions of high-quality research and cutting-edge surveys that will provide readers with the most up-to-date information on innovative big data and deep learning tools in the healthcare field.
• The application of big data analytics in the healthcare industry
• The challenges of secure and encrypted big data processing and analysis in healthcare
• Innovative big data and deep learning tools for improving healthcare intelligence
• Utilizing deep learning for biomedical service delivery, such as remote medical care and telehealth
• The use of big data and deep learning in edge/fog bio-computing nodes and the internet of health things
• The impact of big data and deep learning on healthcare service delivery and patient outcomes
• The ethical and privacy considerations of utilizing big data and deep learning in healthcare
• The future of big data and deep learning in healthcare and biomedical service delivery
Keywords: Big Data, Healthcare, Biomedical services
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.
