A Survey on Transforming Healthcare with IoMT :  The Power of Connected Medical Devices

Dr. D. Antony Arul Raj Arul Raj; Dr. K. V. Rukmani Rukmani; Subiksha S Rukmani; Vimal P Rukmani; Deepak Kumar K

doi:10.32628/CSEIT2410219

Authors

Dr. D. Antony Arul Raj Associate Professor Cum ANO, Department of Software Systems, PSG College of Arts & Science, Coimbatore, India Author
Dr. K. V. Rukmani Associate Professor & Head, Department of Software Systems, PSG College of Arts & Science, Coimbatore, India Author
Subiksha S Department of Software Systems, PSG College of Arts & Science, Coimbatore, India Author
Vimal P Department of Software Systems, PSG College of Arts & Science, Coimbatore, India Author
Deepak Kumar K Department of Software Systems, PSG College of Arts & Science, Coimbatore, India Author

DOI:

https://doi.org/10.32628/CSEIT2410219

Keywords:

Medical Technology, Internet of Medical Things, Wearable Devices, Remote Monitoring, Healthcare Diagnosis, Data Science, Distributed Monitoring, Pregnancy, Reliability, Privacy, Blockchain, Healthcare Monitoring, Fall Prediction, Cardiovascular Health, Machine Learning, Edge-AI, Healthcare Informatics, Anomaly Detection, Security, Cryptography, Patient Care

Abstract

This thorough analysis examines current developments in medical technology, with an emphasis on the Internet of Medical Things (IoMT) and its uses. It covers IoMT's contribution in creating wearable internet monitors for dispersed monitoring systems, addressing pregnancy-related issues, and is divided into four subject groups. Along with blockchain adaption, it also highlights privacy and dependability in IoMT-based health monitoring systems. The paper also emphasizes the contributions of IoMT to healthcare diagnosis and monitoring, including machine learning and edge-AI-based cardiovascular health monitoring and fall prediction. It also covers data science and healthcare informatics techniques, such as using lightweight cryptography to ensure real-time security in remote patient monitoring and identifying unusual user behavior. IoMT has the potential to transform patient care, diagnostics, and data-driven decision-making across a range of healthcare areas, as the paper highlights overall.

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