About this Research Topic
This Research Topic aims to explore the innovative confluence of Artificial Intelligence (AI) and Millimeter Wave (MMW) Radar Imaging within advanced biomedical applications. It seeks to present a curated collection of research that highlights the potential challenges and future directions of this rapidly evolving interdisciplinary technology.
The Research Topic begins with an extensive overview of MMW radar technology, focusing on its physical principles and transformative applications in biomedical contexts, particularly in non-invasive diagnostics and vital signs monitoring. A key part of this exploration is the role of AI, especially the use of deep learning and image processing algorithms, in augmenting the precision and efficacy of MMW imaging.
A significant portion of this Research Topic is devoted to the diverse biomedical applications of AI-enhanced MMW radar imaging, including but not limited to breakthroughs in early-stage cancer detection, stroke diagnosis, and real-time health monitoring. We encourage submissions that offer a comprehensive evaluation of these applications, supported by recent clinical data and trials, to present a balanced view of their current effectiveness and limitations. This Research Topic also aims to address the prevailing challenges in this field, such as data privacy, algorithmic reliability, and the need for extensive, diverse datasets for AI training. Discussions on potential future advancements and the next generation of AI-integrated MMW radar imaging solutions are particularly welcome.
We emphasize the critical role of interdisciplinary collaboration in advancing this field. Submissions should highlight the synergistic approach combining expertise from AI, radar technology, biomedical research, and clinical practice. Through this Research Topic, our aim is to provide a comprehensive platform for academic and industry researchers, clinicians, and technology developers to engage with the latest developments, share insights, and foster collaborations that will drive forward the field of AI-based MMW Radar Imaging in advanced biomedical applications. We invite submissions that extensively cover the adaptation and application of various AI models and algorithms in enhancing MMW radar imaging. Contributions may include original research articles, comprehensive reviews, and detailed case studies. These should not only showcase advancements in imaging quality and diagnostic accuracy but also provide a critical analysis of how AI integration is reshaping the landscape of biomedical imaging, as well as themes including but not limited to:
• Millimeter Wave Imaging
• Radar Technology in Healthcare
• AI Algorithms for Imaging Analysis
• Advanced Biomedical Imaging Techniques
• Clinical Applications of Radar Imaging
• Future of AI and Radar Imaging in Healthcare Diagnostics
• Innovations in Healthcare Radar Technology
• Image Processing Algorithms in Healthcare
• Artificial Intelligence in Biomedicine
• Non-Invasive Medical Diagnostics
• Deep Learning in Medical Imaging
The Research Topic begins with an extensive overview of MMW radar technology, focusing on its physical principles and transformative applications in biomedical contexts, particularly in non-invasive diagnostics and vital signs monitoring. A key part of this exploration is the role of AI, especially the use of deep learning and image processing algorithms, in augmenting the precision and efficacy of MMW imaging.
A significant portion of this Research Topic is devoted to the diverse biomedical applications of AI-enhanced MMW radar imaging, including but not limited to breakthroughs in early-stage cancer detection, stroke diagnosis, and real-time health monitoring. We encourage submissions that offer a comprehensive evaluation of these applications, supported by recent clinical data and trials, to present a balanced view of their current effectiveness and limitations. This Research Topic also aims to address the prevailing challenges in this field, such as data privacy, algorithmic reliability, and the need for extensive, diverse datasets for AI training. Discussions on potential future advancements and the next generation of AI-integrated MMW radar imaging solutions are particularly welcome.
We emphasize the critical role of interdisciplinary collaboration in advancing this field. Submissions should highlight the synergistic approach combining expertise from AI, radar technology, biomedical research, and clinical practice. Through this Research Topic, our aim is to provide a comprehensive platform for academic and industry researchers, clinicians, and technology developers to engage with the latest developments, share insights, and foster collaborations that will drive forward the field of AI-based MMW Radar Imaging in advanced biomedical applications. We invite submissions that extensively cover the adaptation and application of various AI models and algorithms in enhancing MMW radar imaging. Contributions may include original research articles, comprehensive reviews, and detailed case studies. These should not only showcase advancements in imaging quality and diagnostic accuracy but also provide a critical analysis of how AI integration is reshaping the landscape of biomedical imaging, as well as themes including but not limited to:
• Millimeter Wave Imaging
• Radar Technology in Healthcare
• AI Algorithms for Imaging Analysis
• Advanced Biomedical Imaging Techniques
• Clinical Applications of Radar Imaging
• Future of AI and Radar Imaging in Healthcare Diagnostics
• Innovations in Healthcare Radar Technology
• Image Processing Algorithms in Healthcare
• Artificial Intelligence in Biomedicine
• Non-Invasive Medical Diagnostics
• Deep Learning in Medical Imaging
Keywords: millimeter wave, radar, biomedical signal processing, imaging
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.
