The newest topics in the Internet of Medical Things (IoMT) in 2025 focus on advanced integration with AI and big data, enhanced remote patient monitoring (RPM), security and privacy challenges, wireless technology advancements like 5G, and expanding IoMT applications in emerging markets.
There is a strong trend towards Personalized healthcare together with
- AI-driven diagnostics
- Real-time data sharing
- Cloud-enabled scalable IoMT systems.
AI-driven diagnostics
How are AI models being integrated into IoMT diagnostics
AI models are being integrated into IoMT diagnostics through advanced frameworks that combine
IoMT sensors, cloud computing, and sophisticated AI architectures such as transformer-based models. These AI-powered systems analyze physiological data in real-time to enhance diagnostic accuracy and efficiency.
For instance, a novel Transformer-based Self-Attention Model (TL-SAM) processes both spectral and spatial features of signals like heart rate and blood pressure, improving cardiac condition classification with high accuracy (over 98%). This system also includes alert mechanisms to notify healthcare providers in real-time about abnormal patient conditions, enabling quick clinical responses.
Additionally,
AI integration into IoMT diagnostics supports comprehensive data fusion from various sources, including laboratory results, imaging, genomics, clinical history, and real-time sensor data. This enables machine learning models to detect patterns, suggest follow-up diagnostics, and provide personalized diagnostic insights, thereby empowering clinicians to make more informed decisions and tailored treatments. AI is also being employed in diagnostic demand management to optimize testing, reduce unnecessary procedures, and ensure patient safety.
Besides imaging and sensor data analysis,
AI models are used for predicting lab test results from alternative data sources (e.g., estimating hemoglobin levels from images or potassium levels from ECGs).
AI in IoMT also enhances workflows, improves analytical outcomes, and aids in managing chronic diseases.
The connectivity between IoMT devices and central processors using protocols such as Wi-Fi and Bluetooth facilitates continual data collection and AI processing, supporting remote and non-invasive diagnostics.
AI models are integrated at multiple levels—from raw data collection and processing to advanced interpretation and decision support—making IoMT diagnostics smarter, more precise, and capable of operating in resource-limited environments.
AI models are integrated into IoMT diagnostics by combining IoMT sensors with cloud computing and advanced AI architectures such as transformer-based models. These models analyze physiological data like heart rate and blood pressure in real time, improving diagnostic accuracy and enabling early detection of diseases such as heart failure. Systems like the Transformer-based Self-Attention Model (TL-SAM) process spectral and spatial features separately, then fuse them to classify conditions with high precision, supported by alert mechanisms for timely clinical intervention.
Beyond sensor data, AI supports integration of various diagnostic data sources—including lab results, imaging, genomics, and clinical history—into unified platforms that detect patterns, recommend follow-ups, and deliver personalized diagnostic insights, aiding clinicians in decision-making. AI also optimizes diagnostic demand by minimizing unnecessary tests and addressing underuse, improving patient safety.
Moreover, AI enhances remote patient monitoring and chronic disease management by enabling continuous, non-invasive health tracking and analysis. Connectivity protocols like Bluetooth and Wi-Fi link these IoMT devices to central processing hubs for seamless data transmission and AI-driven interpretation.
Summary AI-driven diagnostics
AI models in IoMT diagnostics power sophisticated data processing, real-time monitoring, and comprehensive decision support, making healthcare more accurate, efficient, and accessible even in resource-limited settings.
Real time data sharing in IoMT Systems
Real-time data sharing in Internet of Medical Things (IoMT) systems involves secure and efficient transmission of medical data from IoMT devices (such as wearable sensors) to centralized servers or healthcare providers, enabling continuous monitoring and timely medical decisions.
Data Sharing Mechanism
IoMT systems use protocols like MQTT, which is lightweight and suitable for low-power devices, to transmit encrypted medical data such as patient vitals securely and reliably in real-time.
Data encryption during transmission commonly uses standards like AES-GCM to protect confidentiality and integrity.
The communication framework often incorporates dual-phase authentication to verify devices initially and maintain continuous authentication throughout data transmission, enhancing security.
Real-time data includes vital signs and other physiological parameters collected by sensors that are encrypted, transmitted, decrypted, and processed for medical analysis and decision-making.
Network Architecture and Layers
The perception (sensing) layer collects real-time analog signals from medical sensors and converts them into digital data for further use.
Short-range communication technologies (Wi-Fi, Bluetooth, ZigBee) transmit data from sensors to gateways or network layers within the IoMT ecosystem.
Data pre-processing, such as cleansing and filtering, is done at the perception layer to optimize transmission efficiency and reduce latency.
Security and Performance
The security framework uses lightweight cryptographic techniques tailored for IoMT’s resource-constrained devices to minimize computational load and latency.
Efficient encryption, transmission, and decryption allow for low delay (around 14.59 milliseconds end-to-end latency in some implementations), which is crucial for real-time healthcare monitoring.
Multi-layer security including encryption and device authentication protects against cyber threats like man-in-the-middle, replay, and brute force attacks.
Importance of Real-time Sharing
Real-time data sharing supports continuous patient monitoring and quicker clinical decisions.
It aids remote diagnostics, virtual consultations, and telemedicine by enabling seamless, secure, and timely communication of critical health data.
Summary Real-time data sharing
Real-time data sharing in IoMT systems relies on secure, efficient communication protocols (like MQTT), robust encryption, layered authentication, and optimized architecture for timely and reliable transmission of medical data critical for patient care.
Cloud-enabled and scalable Internet of Medical Things (IoMT) systems
are healthcare-oriented IoT solutions that leverage cloud computing to handle large volumes of medical data and a growing number of devices efficiently. These systems use cloud infrastructure to support the connectivity, storage, processing, and real-time analytics required by IoMT devices such as sensors and medical monitors, enabling remote patient monitoring and healthcare management with flexibility and scalability.
Cloud-Enabled IoMT Systems
Cloud infrastructure acts as a centralized platform that connects medical devices to healthcare providers and patients, enabling secure data transmission and access from anywhere.
These systems allow remote monitoring of patient health through real-time data exchange, with cloud platforms supporting device management, security, and over-the-air updates.
Cloud-based systems also facilitate integration of AI and machine learning to enhance diagnostics, patient risk prediction, and personalized care.
Scalability in IoMT Systems
Scalability means the system can efficiently handle an increasing number of IoMT devices and the vast data they generate without degradation in performance.
Cloud platforms provide elastic scalability, dynamically adjusting resources like storage, computing power, and bandwidth according to demand.
Auto-scaling, load balancing, and container-based services (e.g., Docker) ensure efficient management of peak loads and cost-effective scaling.
Hybrid approaches involving edge and fog computing reduce latency and cloud dependency, improving scalability especially for latency-sensitive medical applications.
Emerging Features in Scalable IoMT Systems
Integration of blockchain and edge computing to enhance security, reduce latency, and decentralize data handling.
Lightweight hybrid authentication mechanisms tailored for IoMT devices with limited resources.
Novel computing layers between dew, fog, and cloud computing layers designed to optimize performance and reduce energy consumption in remote patient monitoring.
Summary cloud-enabled scalable IoMT systems
Cloud-enabled scalable IoMT systems leverage cloud computing to support vast, complex medical device networks with flexible, secure, and efficient data management and processing capabilities. They are designed to grow dynamically with healthcare demands while ensuring real-time performance and regulatory compliance.
Other ongoing IoMT Activities
Partnerships between healthcare and tech giants
are driving innovations, including AI-enhanced RPM systems using federated learning and reinforcement learning for better health outcomes.
Security
remains a critical focus, with blockchain for identity management, lightweight cryptographic protocols, and AI-driven intrusion detection being explored for safeguarding IoMT networks.
Emerging wireless technologies enable faster, low-latency connectivity essential for real-time monitoring but also increase the exposure to cyber threats, which need addressing in device and system design.
Additionally, IoMT is increasingly essential for remote care, chronic disease management, and improving healthcare access in underserved regions worldwide.
All these trends reflect that IoMT is central to the future of smart, connected healthcare ecosystems emphasizing accessibility, efficiency, safety, and personalization.
Sources
Newest topics in IoMT Internet of Medical Things
- https://inspital.com/future-of-medical-technology-what-trends-should-clinics-and-hospitals-keep-in-mind-for-2025/
- https://www.ul.com/insights/securing-future-internet-medical-things-2030
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AI-driven diagnostics
- https://medcloudinsider.com/articles/2025/08/01/ai-and-iomt-cloud-integration.aspx
- https://jlpm.amegroups.org/article/view/10311/html
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Real time data sharing in IoMT Systems
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- https://arxiv.org/html/2504.02446v1
- https://www.ezurio.com/resources/blog/top-applications-of-the-internet-of-medical-things-iomt-in-healthcare
- https://www.sciencedirect.com/science/article/pii/S2405844024032080
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- https://pmc.ncbi.nlm.nih.gov/articles/PMC11986172/
Cloud-enabled and scalable Internet of Medical Things (IoMT) systems
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- https://www.biot-med.com/resources/how-to-choose-the-right-iomt-cloud-infrastructure-for-medical-devices
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- https://ceti.reapress.com/journal/article/download/26/21
- https://punchthrough.com/cloud-connected-medical-devices-considerations/
- https://www.nature.com/articles/s41598-025-09727-z
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- https://euristiq.com/best-iot-cloud-platforms/
