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The Internet of Medical Things (IoMT) facilitates patients with all-time-connected medical devices through cost-effective solutions and a feeling of comfort with round-the-clock hospital support. The patients who cannot visit hospitals for routine checkups prefer the usage of IoMT devices. The healthcare facilities also rely on the real-time statistics of IoMT machines and diagnose problems and their solutions within a small interval of time. This could only be possible when a robust convergence of technologies is used with IoMT devices. This review study discusses how we may apply the convergence of IoMT devices with trending technologies and focuses on delivering a new dimension of novel IoMT usage through broad multi-homing dense networks. It also discusses various applied machine learning algorithms available in the healthcare industry. It compares a variety of disease predictions and the accuracy of the equipment and decision recommendations. The review study also discusses various IoMT convergence open challenges and opportunities through the labor industry healthcare system.
Wagan et al. (Sat,) studied this question.
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