Applications of Deep Learning and Sensing Technologies in Healthcare Monitoring

Dear Colleagues,

Artificial intelligence (AI) is now considered to be one of the most important areas of digital science and healthcare research. Artificial intelligence is now regarded as the source of breakthroughs for a new generation of industrial innovation. The COVID-19 pandemic has also accelerated investment in artificial intelligence, especially in healthcare.  The clinical impact of machine intelligence has enormous potential to enhance healthcare, making it more feasible, accessible and affordable. However, applications are still in their early stages due to numerous industrial constraints and technical limitations of AI such as the the lack of explainability. A complex set of ethical considerations also affect the speed of AI deployment. Currently, there is no clear definition of legal responsibility for AI systems in healthcare, which has raised concerns for both doctors and patients.

The objective of this Special Issue is to generate a comprehensive understanding of medical AI and biosensors in clinical applications. It will also highlight the latest industrial solutions to recent challenges for AI deployment in clinical settings. Authors are invited to submit outstanding and original unpublished research manuscripts focused on the latest findings in related fields. The topics of interest are limited to the following:

• Artificial intelligence for medical imaging;
• Explainable artificial intelligence in healthcare;
• Artificial intelligence for healthcare sensing and monitoring;
• Interventional tracking and navigation;
• Medical robotics and haptics;
• Biosensors;
• Biosensing technique;
• Wearable devices;
• Medical artificial intelligence;
• Diagnosis of artificial intelligence;
• Predication of medical artificial intelligence;
• Artificial intelligence ethical study in medicine;
• Medical imaging understanding;
• Image segmentation, registration, and fusion;
• Image reconstruction and image quality;
• Computer-aided diagnosis;
• Population imaging and imaging genetics;
• Applications of big data in imaging;
• Integration of imaging with non-imaging biomarkers;
• Visualization in biomedical Imaging;
• Interventional imaging systems;
• Image-guided interventions and surgery.

Dr. Tao Tan
Dr. Jiangang Chen
Dr. Fan Huang
Dr. Xiayu Xu
Dr. Mengze Xu
Guest Editors

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