Towards AI-Enabled Internet of Medical Things

Scope of the papers

In recent years, the Internet of Medical Things (IoMT) has shown the potential to revolutionize the healthcare industry. IoMT encompasses a network of interconnected healthcare devices and systems that gather, analyze, and transmit health data. This enables remote patient monitoring, diagnosis, and treatment, dramatically changing the way healthcare is delivered. The efficient integration of digital technologies, defined by systematic and appropriate application, requires adopting a structured management approach that includes strategic network planning, resource allocation, and control and evaluation processes. These elements are fundamental to improving healthcare services, equipment, administration, and technologies. The benefits of artificial intelligence (AI) across various healthcare domains, including cancer diagnosis, biomarker imaging, cardiac monitoring, diabetes management, and surgical assistance, have been documented. Machine learning (ML) enables efficient processing of large, complex sensor datasets, thereby enhancing subsequent analysis and supporting improved clinical decision-making. Additionally, AI and ML techniques enable the extraction of analytical information from low-resolution or noisy datasets. This Special Session aims to disseminate emerging trends and recent developments in IoMT networking, focusing on cost-effective wearables, AI/ML models for disease prediction, biomedical imaging and signal processing, IoMT data management, and security.

Topics of interest include, but are not limited to:

• Internet of Medical Things, medical sensors, and medical devices
• Wireless medical sensor technologies and wireless, mobile, and smart wearable devices for pervasive healthcare
• 5G/6G-based IoMT
• Innovative communication and mobile technologies to support data collection and access, sharing, and storing
• AI/ML-driven predictive modeling for disease prevention
• AI/ML-driven clinical decision support systems
• Biomedical imaging and signal processing
• Digital Twin architectures for healthcare
• VR/AR systems for healthcare systems
• Data management and storage optimization