Internet of Medical Things (IoMT): Transforming Remote Patient Monitoring Through Artificial Intelligence

Chelsea Finn

Internet of Medical Things (IoMT): Transforming Remote Patient Monitoring Through Artificial Intelligence

Author(s)	Chelsea Finn
Country	United States
Abstract	The rapid advancement of digital healthcare technologies has significantly transformed modern healthcare delivery systems, enabling personalised, accessible, and efficient medical services. The Internet of Medical Things (IoMT) has emerged as a revolutionary healthcare framework that integrates connected medical devices, wearable sensors, Internet of Things (IoT) infrastructure, cloud computing, and Artificial Intelligence (AI) to facilitate continuous remote patient monitoring and intelligent healthcare decision-making. Traditional healthcare systems primarily depend on periodic clinical visits, which may delay disease detection and limit continuous monitoring of chronic health conditions. IoMT addresses these limitations by enabling real-time collection, transmission, and analysis of patient health data. This study examines the role of IoMT in transforming remote patient monitoring through Artificial Intelligence using a qualitative and analytical research methodology based on secondary data collected from scientific literature, healthcare technology reports, and digital health case studies. The research explores IoMT architecture, AI-driven health analytics, wearable medical devices, predictive healthcare models, remote diagnosis support, chronic disease management, and personalised healthcare applications. The findings indicate that AI-enabled IoMT systems significantly improve healthcare outcomes by enabling early disease detection, predictive risk assessment, continuous monitoring, and personalised treatment recommendations. Machine Learning and Deep Learning algorithms analyse real-time physiological data including heart rate, blood pressure, glucose levels, oxygen saturation, and respiratory patterns to identify abnormalities and support clinical decision-making. Integration with cloud computing, edge intelligence, blockchain, and 5G networks further enhances scalability, security, and reliability. However, challenges related to data privacy, cybersecurity threats, device interoperability, regulatory compliance, technological costs, and digital accessibility remain significant barriers to widespread IoMT adoption. Addressing these challenges requires robust security frameworks, ethical AI governance, standardised communication protocols, and collaboration among healthcare providers, technology developers, policymakers, and patients. The study concludes that Artificial Intelligence-driven IoMT represents a transformative approach for future healthcare systems by shifting medical care from reactive treatment towards proactive, predictive, and patient-centred healthcare management.
Keywords	: Internet of Medical Things, IoMT, Artificial Intelligence, Remote Patient Monitoring, Digital Healthcare, Machine Learning, Wearable Devices, Predictive Healthcare, Smart Healthcare.
Field	Engineering
Published In	Volume 1, Issue 3, May-June 2019
Published On	2019-05-02

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Internet of Medical Things (IoMT): Transforming Remote Patient Monitoring Through Artificial Intelligence

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