Wired for wellness: How IoMT is rebooting global healthcare

Devices such as smartwatches, biosensors, and implantable monitors are now routinely used to track blood pressure, glucose levels, heart rate, and more. The health data generated by these devices can be processed through cloud computing and artificial intelligence platforms to support diagnostics, treatment decisions, and patient management.

A comprehensive review published in the journal Sci concludes that the Internet of Medical Things (IoMT) is rapidly transforming healthcare delivery, diagnostics, and management, with implications for hospitals, patients, policymakers, and technology developers alike. The study, conducted by researchers at Edge Hill University, examines the current state, architecture, applications, and challenges of IoMT technologies and argues that their integration is redefining modern healthcare infrastructure.

Titled "A Review of the State of the Art for the Internet of Medical Things", the study evaluated the entire IoMT ecosystem, including wearable devices, hospital-based sensors, smart infrastructure, and emerging applications in assistive living, remote monitoring, and fitness tracking.

Before diving into the details, it's important to understand what IoMT) is and why it plays a critical role in the future of healthcare. In simple terms, IoMT refers to a system of interconnected medical devices, software applications, sensors, and data platforms that monitor and transmit health information in real-time.

IoMT devices are often classified into three categories: wearable, implantable, and stationary. Wearables include fitness trackers and smartwatches; implantables include devices such as insulin pumps and pacemakers; and stationary systems comprise hospital tools like infusion pumps and CT scanners. The devices are enabled by connectivity standards such as Wi-Fi, Bluetooth, 5G, and protocols like 6LowPAN and WirelessHART. 

Devices such as smartwatches, biosensors, and implantable monitors are now routinely used to track blood pressure, glucose levels, heart rate, and more. The health data generated by these devices can be processed through cloud computing and artificial intelligence platforms to support diagnostics, treatment decisions, and patient management.

The researchers found that the use of cloud platforms, including AWS HealthLake and Google Cloud Healthcare API, has enabled the storage and analysis of large volumes of patient data. These services support interoperability, data-driven insights, and integration with electronic health records, making personalized treatment and long-term health trend analysis possible.

The report also examines the layered architecture supporting IoMT systems. At the core is the edge layer, where IoMT devices gather and transmit data. These connect to fog computing nodes that process data locally to reduce latency, particularly in time-sensitive applications like seizure monitoring or fall detection. The final layer is the cloud, where large-scale analysis, machine learning, and long-term data storage take place.

In practical terms, the study highlights how IoMT is already embedded in modern healthcare settings. Hospitals are increasingly using robotic systems to assist doctors and nurses, automate medication delivery, and support surgeries with precision tools. Remote monitoring devices now allow for real-time observation of patients with chronic conditions, reducing hospital visits and enabling proactive care. In assistive living environments, IoMT platforms are designed to support elderly individuals by detecting falls, reminding them of medications, and alerting caregivers in emergencies.

One of the review’s significant contributions is the analysis of commercially available IoMT devices. These include the FreeStyle Libre glucose monitor, which transmits data every minute; the Cardionica, which detects atrial fibrillation through ECG scans; and the Oura Ring, which uses sensors to track sleep, heart rate, and recovery. These products are already in use by consumers and clinicians, offering real-world validation of IoMT’s utility.

However, the researchers caution that the adoption of IoMT is not without obstacles. Data security remains a primary concern, with continuous real-time health monitoring raising the stakes for potential breaches. The review emphasizes the importance of layered security models, including encryption, biometric protection, and blockchain-based data handling.

User acceptance is another hurdle. The study notes that despite growing public interest, many patients remain skeptical of medical technologies, especially those that collect personal data continuously. Device design, comfort, and perceived intrusiveness play a critical role in determining adoption, particularly among older users and vulnerable populations.

Infrastructure and technical challenges were also identified. The volume and complexity of data generated by IoMT devices demand robust processing capabilities. Delays in data transmission or failure to synchronize devices can compromise patient safety. Wearability, battery life, and device interference are additional limitations that designers must address.

The authors argue that collaboration among stakeholders is essential for successful implementation. They call for standardized interoperability protocols, expanded broadband access, and ethical guidelines to govern data use. The study emphasizes that strategic integration of IoMT into national healthcare systems could reduce costs, enhance precision, and shift healthcare delivery toward a more proactive and personalized model.

Although the field continues to evolve rapidly, the researchers remain optimistic about IoMT’s future. They highlight the technology’s potential to enable early disease detection, automate routine medical tasks, and support population health management. When combined with artificial intelligence and big data analytics, IoMT systems can identify health patterns that inform clinical decision-making and public health interventions.