Background: The Internet of Medical Things (IoMT) connects sensors, wearable and implantable devices, gateways, networks, computing platforms, electronic health records, and decision-support services. Its value depends on valid measurement, dependable communication, workflow integration, cybersecurity, interoperability, and accountable analytics.

Objective: To provide a transparent structured narrative synthesis of contemporary IoMT technologies, clinical deployments, and comparative architecture trade-offs healthcare-specific security and interoperability requirements, and the maturity of federated learning, digital twins, explainable Artificial Intelligence (AI), and secure AI.

Methods: PubMed/MEDLINE and targeted searches of IEEE Xplore, Scopus, Web of Science, ACM Digital Library, and Google Scholar were used to identify English-language evidence published primarily from January 2021 to September 20, 2026; seminal standards and foundational studies were retained when needed. Searches combined IoT/IoMT terms with healthcare, wearables, remote monitoring, edge/fog/cloud, 5G, THz, cybersecurity, interoperability, federated learning, digital twins, and explainable AI. This is a structured narrative review, not a PRISMA systematic review;
evidence was prioritized by clinical relevance, recency, methodological strength, and deployment maturity (Baethge, Goldbeck-Wood, & Mertens, 2019; Page et al., 2021).

Results: Thirty core sources were retained, including primary clinical and implementation studies, systematic/scoping reviews, technical reviews, standards, regulatory guidance, and a 2026 analytical study of mobility-aware THz reliability. Bluetooth Low Energy is suited to low-power wearable links; Wi-Fi to high-throughput managed environments; 5G to mobile wide-area services; and wireless body area networks to coordinated on-body sensing. Edge computing reduces response time and data exposure, whereas cloud computing improves elasticity while increasing connectivity
and concentration risks. Secure lifecycle management and clinically relevant testing are more appropriate than generic antivirus rankings. Federated learning and explainable AI are translational, while patient digital twins remain predominantly preclinical (S. R. Abbas, Z. Abbas, Zahir, & Lee, 2024; Ahmadi et al., 2019; Allen, 2024; Drummond & Gonsard, 2024; Freyer, Groß, & Lipprandt, 2024; Health Level Seven International, 2023; Hernandez-Jaimes, Martínez-Cruz, Ramírez-Gutiérrez, & Feregrino-Uribe, 2023; IEEE Standards Association, n.d.; Lu et al., 2024; Messinis et al., 2024; National Institute of Standards and Technology, 2021; Nguyen et al., 2026; Panahi, 2026; Patel et al., 2023; Rani, Kataria, Kumar, & Tiwari, 2023; Rosenbacke, Melhus, McKee, & Stuckler, 2024; Shen, Chen, & Ding, 2024; Sun et al., 2018; Teo et al., 2024; Torab-Miandoab, Samad-Soltani, Jodati, & Rezaei-Hachesu, 2023; U.S. Food and Drug Administration, 2026; van Kolfschooten & van Oirschot, 2024; Wettstein et al., 2024; Zakerabasali & Ayyoubzadeh, 2022).

Conclusions: IoMT is moving from isolated pilots toward integrated clinical infrastructure, but readiness remains uneven. Near-term deployment should prioritize validated sensing, resilient hybrid edge-cloud architectures, standardsbased exchange, lifecycle cybersecurity, explicit clinical escalation, and prospective outcome evaluation. Beyond-5G/THz links may support future high-data-rate services, but mobility-related beam misalignment and tracking delay must be included in reliability and outage analysis (Panahi, 2026).
Keywords: Internet of Things; Internet of Medical Things; IoMT; remote patient monitoring; edge AI; cybersecurity; interoperability; federated learning; digital twins; explainable AI; terahertz communication.