Medical devices and telehealth transformation

According to Oleg Bestsennyy in McKinsey’s 2025 Healthcare Shift report, IoMT technology is essential for addressing the key challenges in healthcare of personalised care, treatment adherence, efficiency and reducing preventable hospitalisation by up to 25%. Over time, IoMT has expanded into applications such as remote-assisted surgery, elderly care in the home, post-operative follow-up, chronic disease management and faster, more accurate diagnostics.

Advances in IoMT technology have been driven by innovations in equipment and communication architectures. In terms of equipment, miniaturised, non-invasive and highly accurate sensors, coupled with advanced biosensors, have significantly advanced connected health systems. Needle-free glucose monitoring devices are now commonplace, as are wearable biosensors such as the latest ST1 VAFE3BX from French-Italian semiconductor leader STMicroelectronics. This new sensor measures parameters such as accelerometry, gyroscopy, and bio-signals (ECG, EEG, EMG) within a compact 2x2mm design, enabling real-time monitoring of health metrics.

Wireless communication technology forms the backbone of IoMT infrastructure. Various protocols are tailored to specific applications, including Wi-Fi for ventilators, RFID for infusion tests and asset tagging, and Bluetooth for patient monitoring. ZigBee is used for remote patient monitoring and smart health sensors. “An ultra-wideband (UWB) radar-based IoMT system has even been developed to monitor vital signs in elderly individuals as they go about their daily activities”, explains Inas Al Khalib, a research associate specialising in IoMT at the Department of Industrial Engineering, American University of Sharjah, United Arab Emirates.

A significant trend in IoMT architecture is the integration of edge and fog computing. Edge computing brings data processing closer to IoT devices, with mini-servers such as Raspberry Pi executing algorithms for data pre-processing. Fog computing adds an intermediate layer between IoT devices and the cloud, decentralising data storage and management. These architectures reduce latency by 40–50%, improving resource management and network efficiency.

Edge AI is transforming remote healthcare systems. The global telehealth market, projected to grow at a CAGR of 24.3%, will reach $455.27 billion by 2030, according to Grand View Research. The integration of artificial intelligence (AI) into IoMT and telemedicine platforms has enhanced healthcare accessibility and efficiency worldwide. Edge AI, where AI algorithms operate directly on IoMT devices, reduces dependency on the cloud and offers a variety of benefits.

Edge AI reduces latency by up to 60%, enabling faster response times – critical for timely interventions. It also lowers bandwidth and storage costs by 40%, reducing operating costs for providers. Moreover, autonomous Edge AI devices improve care accessibility, benefiting up to 70% of patients in rural areas with limited connectivity. By doing so, Edge AI helps bridge the digital divide in healthcare, improving access to quality care.

Med-tech companies and digital technology giants have increasingly formed partnerships to develop large-scale telehealth solutions. Initially, companies like Philips Healthcare launched platforms such as HealthSuite, which collects, stores, and analyses health data from connected devices to monitor patients both in hospital and at home, while supporting clinical decision-making using AI analytics.

More recently, Medtronic partnered with Amazon Web Services (AWS) in July 2024 to create scalable telehealth solutions, leveraging AWS’s cloud capabilities to enhance remote healthcare. Similarly, Philips expanded its partnership with AWS in November 2024, offering integrated diagnostic tools in radiology, digital pathology, and cardiology via the cloud, streamlining workflows and improving access to critical data. These partnerships highlight the convergence between med-tech and digital technology, revolutionising healthcare delivery through AI and IoMT.

While IoMT has immense potential to enhance patient outcomes and reduce healthcare costs, it also raises significant cybersecurity concerns. “Connected medical devices are prime targets for hackers,” said Kevin Mandia, founder of Mandiant. The exposure risk of IoMT devices to cyberattacks is high, with 50% of devices in hospitals facing critical vulnerabilities. A December 2024 analysis by Health-ISAC in Denver revealed 12 devices from five manufacturers had been compromised by cybercriminals. Furthermore, Wallix, a French cybersecurity firm, reported that 80% of medical technology firms have experienced cyberattacks in the last five years, underscoring the vulnerabilities in IoMT devices. The cost of these attacks is significant. An IBM report in 2024 found that the average cost of a healthcare breach reached $9.8 million in 2024, a 37.4% increase from 2020. Cybercriminals often hijack devices with weak security to launch attacks on other systems, putting patient data and healthcare infrastructure at risk.

To address these risks, healthcare organisations must implement robust cybersecurity measures. Chief Information Security Officers (CISOs) and cybersecurity experts play a crucial role in ensuring adherence to security protocols, including real-time anomaly detection and breach prevention. Adopting a “security by design” approach, which integrates security into the development of IoMT devices, is essential. Advanced cryptography and blockchain solutions are critical for securing sensitive data.

“Cryptography, including lightweight algorithms, ensures the confidentiality, integrity, and authenticity of IoMT data,” explains Inas Al Khalib from the American University of Sharjah, United Arab Emirates. Symmetric encryption, asymmetric encryption for key exchanges, and hashing are key techniques for maintaining data integrity. Blockchain technology provides a secure, immutable storage solution for medical data, with encrypted blocks and consensus algorithms such as Proof of Work or Proof of Stake to validate transactions.

1960s–1970s

• Development of the first connected medical devices, including remotely monitored pacemakers.
• Emergence of telemedicine concepts, featuring teleconsultations conducted via telephone.

1980s–1990s

• Introduction of telemetry in healthcare, enabling real-time monitoring of patient vital signs in hospital.
  
• Early experiments with remote-controlled surgery.

2000s

• Miniaturisation of medical sensors.
• Advances in the Internet of Things (IoT) paved the way for medical integration.
• Launch of mobile applications for managing chronic diseases, such as diabetes.

2010s

• Massive expansion of wearable devices, including smartwatches and fitness trackers.
• Integration of medical data into cloud platforms.
• Clinical applications flourished: remote monitoring for chronic diseases, virtual rehabilitation, and early alerts for medical emergencies.

2020s

• Surge in telemedicine and IoMT device adoption, driven by the COVID-19 pandemic.
• ​​​​​​​Rise of virtual hospitals, AI-enhanced home care, and highly integrated wearable devices.