Smarter Healthcare: The AI Advantage in Medical Devices

When you're designing medical devices, adding AI that can identify fractures in X-rays or subtle patterns in vitals feels like giving your hardware a sixth sense.

Venture capital funding in AI medical devices topped $3.6 billion globally in 2023, paving the way for more forward-thinking startups to innovate in healthcare technology. Designing a compact new patient monitor or fine-tuning IoT Gateway firmware to ensure faster computation is a significant step forward. However, to truly unlock the potential of these advancements, AI integration is essential. Real-time decision-making through embedded AI is what elevates hardware from being just efficient to being truly intelligent and transformative.

The Transformative Impact of AI in Healthcare

According to McKinsey, AI and machine learning in healthcare could deliver up to $100 billion annually in savings by improving efficiencies, enhancing preventive care, and optimising treatment innovation. Meanwhile, a report from Deloitte projects that AI-enabled medical devices will grow at a compound annual growth rate (CAGR) of over 29% through 2030, transforming how healthcare organisations prioritise tech investments.

AI isn't just enhancing medical devices; it's fundamentally reshaping how we engineer them. From compact, portable ultrasound machines that outperform traditional diagnostics to next-gen ECG patches that can detect arrhythmias weeks before symptoms appear.

Enhancing Diagnostic Accuracy with AI

AI's biggest wins right now are in diagnostics and predictive analytics. Studies show AI can analyse vast amounts of medical data with unprecedented speed and precision. 

For example, AI Radiology Assistants can now detect conditions such as coronary artery disease and breast cancer from imaging scans with remarkable speed, reducing workload and turnaround times for hospitals.

Real-World Applications of AI in Medical Devices

Portable Ultrasound: Handheld devices enable diagnostics in remote and low-resource settings.

Smart ECGs: Real-time ECGs from a smartphone, aiding early detection of atrial fibrillation.

AI-Powered Endoscopy Systems: Detect precancerous lesions in real-time using.

Glucose Monitors with Predictive AI: Predict glucose fluctuations via miniaturised biosensors.

AI-Assisted Hearing Aids: Enhance speech clarity with ultra-low-latency neural audio processors.

Neural Interface Prosthetics: Control prosthetic limbs using neural signals with real-time processing.

AI-Integrated Smart Inhalers: Predict asthma attacks using precision sensors and wireless AI analytics.

Ophthalmic Imaging Devices: Detect eye diseases using high-resolution cameras and AI processing units.

Wearable Seizure Detection: Monitor for seizures using low-power AI-enabled processors.

Robotic Rehabilitation Systems: Personalise therapy with embedded AI control systems.

The role of Edge Computing AI-Enabled Medical Devices

Edge computing brings data processing closer to the source, minimising latency and enabling faster responses in medical devices. This local approach supports real-time decision-making and reduces the risk of data exposure by keeping processing on-device rather than in the cloud.

For example, AI-enabled wearable devices can continuously monitor vital signs and promptly alert healthcare providers when necessary.

Using hardware with high TOPS (Terra Operations Per Second) such as the Fitlet3 with Hailo is important because it helps quickly and accurately process complex clinical data for real-time AI medical detection. However, for simpler tasks or basic computations, hardware with lower TOPS ratings, for example the iMX95 System on Module is usually sufficient and cost-effective.

To meet the computational demands of AI algorithms, high-performance embedded systems incorporate features like secure boot, encryption, and hardware-based security modules. 

These elements ensure data integrity and protect against unauthorised access, making AI at the edge both efficient and secure.

Compliance and Cyber Security in AI Medical Devices

Embedding AI in medical devices also requires strict adherence to regulatory standards such as ISO 13485 which mandates quality management practices for medical devices. The recently introduced Cyber Resilience Act (CRA) emphasises robust security to protect connected medical systems from cyberattacks. Ensuring compliance with these frameworks is essential for maintaining device reliability and patient safety.

Anders is an ISO13485 certified company and committed to staying at the forefront of regulatory excellence and cybersecurity best practices, helping our partners navigate CRA requirements with the same care and precision that we bring to every design. Our hardware, display, and IoT solutions are engineered to complement the intelligence AI brings, creating trusted systems that patients can rely on.

The future for AI powered medical devices

IDC predicts that by 2026, over 45% of clinical decisions will be supported by real-time data collected from AI-powered devices. Predictive health monitoring woven into wearables, real-time surgical guidance, and dynamic treatments are just the beginning. AI doesn't replace human intelligence; it amplifies it, blending hardware, software, and data to deliver more effective medical diagnostics and treatments.

At Anders, we are committed to pushing the boundaries of medical innovation. By integrating AI seamlessly into hardware platforms, we enable healthcare providers to deliver smarter, more secure, and highly efficient care. 

Contact us to discuss your next breakthrough medical product.