Collaborative Medical Smart Spaces for an Enhanced Health Issues Detection
Saad El Jaouhari · 2023
The current advancements in technology have paved the way for innovative approaches to improve healthcare services, including the Machine Learning and and the Internet of Things (loT). More precisely, Internet of Medical Things (loMT), have shown their results in providing a rapid medical assistance to the medical staff in order to better serve their patients. The latter refers to the network of interconnected medical devices, wearable gadgets, sensors, and equipment that can collect, transmit, and share medical data and information via the internet. On the other hand, Machine learning (ML) has emerged as a transformative technology in the healthcare sector, revolutionizing how medical data is analyzed and utilized. Multiple applications of ML in healthcare have been deeply analyzed and discussed such as the enhancement of disease diagnosis, treatment optimization, and surgical operations. Nonetheless, the insights gained by these trained ML models remain confined to the data collected within the immediate environment and the distinct characteristics of each patient's context. Furthermore, the reluctance to share these models securely with external systems and entities hinders the progression of such solutions toward a broader, more accurate, and resilient framework. Therefore, in this paper, a special focus is given to specific field of ML which is Federated Learning (FL). The main objective is to discuss the potential of to sharing already trained ML models across multiple entities for a better health issues detection. Such trained models already contains knowledge that can be directly used by other entities to either detect an already known health issues or new ones. Thus, this paper presents a comprehensive study on the synergistic utilization of these technologies to share medical knowledge while addressing privacy concerns and enable personalized healthcare solutions. We explore the theoretical foundations and potential applications of integrating Federated Learning and 10MT in remote monitoring.