Transformative impacts of the internet of medical things on modern healthcare

Abstract

The Internet of Medical Things (IoMT) offers real-time data insights, reduces energy costs, and enhances patient comfort, presenting transformative potential for healthcare systems. While existing research on IoMT in healthcare has primarily concentrated on data security concerns and corresponding solutions, it has often overlooked the broader role of IoMT technologies across diverse healthcare systems and the unique challenges of implementing them. This study aims to bridge these gaps by investigating how IoMT impacts healthcare systems, focusing on its technological roles, benefits, and challenges. The significance of this work lies in its comprehensive exploration of IoMT applications and their potential to transform healthcare delivery through personalized treatment, diagnostics, and enhanced quality of care. The findings indicate that combining IoMT with machine learning (ML) can achieve up to 99.84 % accuracy in predicting heart disease from medical images, while remote monitoring for elderly patients reaches an accuracy of 98.1 %. Additionally, a model utilizing edge-IoMT computations demonstrates a promising solution for real-time seizure detection. By facilitating continuous data collection and providing real-time insights, IoMT significantly enhances the operational effectiveness of ML algorithms, ultimately leading to improved health outcomes for these vulnerable populations. However, IoMT integration into smart healthcare systems raises security concerns, which can be mitigated by using strong encryption, authentication, and security updates, following privacy regulations, and educating healthcare professionals about cybersecurity. Future IoMT research must prioritize the implementation of artificial intelligence algorithms to improve the management of security and vulnerabilities in intelligent healthcare systems.