Machine Learning for Internet of Medical Things Applications
Divneet Kaur, Bharatdeep Singh, Sita Rani
Abstract
The integration of machine learning and the Internet of Medical Things (IoMT) is revolutionizing healthcare delivery by leveraging data generated from connected medical devices. In this work, the authors explore the symbiotic relationship between ML and IoMT, highlighting their combined potential to reshape healthcare practices. ML algorithms, operating within the framework of IoMT, analyze vast datasets to uncover patterns and correlations essential for personalized medicine, diagnostic imaging, remote patient monitoring, predictive analytics, and emergency medical services. However, challenges such as ethical concerns, data security, interoperability, and regulatory compliance must be addressed to fully harness the benefits of this integration. Looking forward, collaboration opportunities, emerging applications, and advancements in ML technology are explored to provide insights into the evolving landscape of ML and IoMT in healthcare. Understanding the dynamics of this convergence is crucial for maximizing its transformative impact on healthcare, driving toward more personalized, efficient, and data-driven healthcare system.