Next-Generation Telemedicine: Integrating AI with Advanced Wireless Networks

The intersection of wireless communication technologies and telemedicine has become a pivotal area of research in healthcare technology. Recent advancements in 5G, 6G, mmWave, and massive MIMO are transforming how medical data, especially high-resolution images like X-rays and MRIs, are transmitted across distances without quality degradation. These advancements are crucial for enhancing precision medicine by providing low-latency, high-speed data transfer capabilities allowing real-time, high-quality image processing. This capability is especially significant in delivering personalized healthcare solutions tailored to individual patient needs, even in remote or underserved areas, which can potentially improve patient outcomes and satisfaction. However, despite the promising potential of telemedicine, the full potential of telemedicine is still hindered by challenges such as signal interference, data privacy issues, limited rural coverage, and energy consumption in portable devices. Addressing these barriers will be essential in leveraging wireless communication technologies to provide personalized and effective healthcare services on a broader scale.

This Research Topic aims to delve into the integration of AI and edge computing with these sophisticated wireless networks to enhance telemedicine platforms. By facilitating real-time image analysis and decision-making at the data source, this integration aims to reduce latency, optimize resource usage, and improve the efficiency and response time of healthcare services. The focus will be on overcoming prevalent challenges through efficient resource management, effective data compression, and secure communication protocols. It will enable the provisioning of accessible, high-quality, and safe healthcare services across varied geographic regions.

We invite original research and review articles that contribute to the following areas:

o AI-driven techniques for image enhancement and anomaly detection within telemedicine
o Integration of 6G and edge computing to improve telemedicine services
o Innovative solutions for robust data transmission and privacy in telemedicine
o Development of new wireless technologies like reconfigurable intelligent surfaces (RIS) and filter-bank multicarrier (FBMC) for healthcare applications
o Studies on UAV-assisted and cloud-based medical image processing and transmission

Through these contributions, this Research Topic aims to provide groundbreaking solutions that enhance the accessibility and quality of telemedicine, making remote healthcare more effective and precisely.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Anomaly detection in medical imaging, Remote healthcare solution, and robust data compression, MIMO, Artificial intelligence (AI), Wireless telemedicine framework

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.