Innovating Medical Technologies: Rapid Development of Medical Grade AI - A Success Story

Camgenium's CEO Dr Philip Gaffney OBE shared his MedTech insights at Digital Health World Congress 2025. Read Philip's full presentation transcript below.

For the past 18 years, I’ve worked in regulated MedTech, leading Camgenium to specialise in AI, Software as a Medical Device, and connected medical devices.

Our mission is clear: to reduce regulatory burdens, lower costs, and shorten development timelines. We offer a one-stop shop for white-labelled device development and deployment, whether you need end-to-end solutions or support for a specific part of the lifecycle.

With a global operation, we’re positioned to deliver innovation worldwide.

Let’s dive into why this matters.

Look at this image—a diverse crowd of people, from children to the elderly, across all demographics. Each person is unique, and so are their healthcare needs. The inset image shows one individual’s journey from childhood to old age, reminding us that health evolves over time.

Now, consider the blood pressure chart. It shows wide ranges for what’s considered normal, healthy, or high. One size does not fit all.

This is where personalised medicine comes in. By leveraging AI, we can analyse data from large populations—millions of records—to identify predictors of disease. AI models can then be tailored to individual factors like age, demographics, or lifestyle. This enables intelligent medical devices that adapt to the patient, delivering precise, personalised care.

Today, I’ll show you how we’re making this a reality.

Let me share a success story about a product we developed for our partner, C2-Ai. Their AI-powered waiting list model is now the most widely deployed within the NHS. It predicts an individual’s risk of complications following surgery, built on a foundation of data from millions of patient records.  At the height of Covid, we developed and deployed the first version of this product in just two months.

This case demonstrates the power of rapid development and AI-driven insights in addressing critical healthcare challenges. It’s a blueprint for what’s possible when innovation meets urgency.

Building and deploying medical devices is no small feat. It requires a large, multidisciplinary team with expertise in medicine, engineering, AI, and regulatory compliance. In fact, regulatory compliance accounts for up to 60% of the development burden—time, cost, and complexity that can stall innovation.

Our solution? Focus on developing new intellectual property to meet rigorous standards, but without re-inventing the wheel.

We create pre-existing, certified modules and capabilities for features that are common to medical devices.

For many of our partners, we also take on the responsibility of regulatory compliance, freeing them to focus on innovation and commercial success. This approach cuts costs and shortens timelines, making high-quality medical devices more accessible.

Now, let’s talk about the future: edge AI and the connected patient.

Imagine a patient surrounded by sensors in an intelligently monitored environment, as shown here. We’re shifting healthcare from cure to prevention. Wearables with built-in AI provide real-time, individually personalised interpretation of data from parameters such as blood pressure, heart rate, or glucose levels.

These devices use multi-parametric monitoring and combine multiple data points for a holistic view of health. A high-performance network ensures continuous connectivity and resilience, while centralised predictive assessments identify risks before they become emergencies. Edge AI brings intelligence directly to the device, benefiting patients and empowering providers.

This slide is the heart of our story—it showcases the breadth and impact of our AI-driven medical technologies. Let me walk you through four key examples that highlight our work since 2015.

First, consider our earliest project: real-time ultrasound image analysis for 12-week baby scans during pregnancy. Our AI identifies anatomical features with precision, helping clinicians detect potential issues early. This was groundbreaking in 2015.

Next, we’ve developed AI models that automatically identify trends and features that signal potential health risks, such as rising biomarkers or subtle changes in vitals. This allows clinicians to intervene proactively, improving outcomes.

We’ve also pioneered knowledge graphs as a decision-making tool. These graphs map complex relationships between patient data, symptoms, and treatments, providing clinicians with clear, actionable insights. It’s like giving doctors a roadmap to navigate the complexity of human health.

Finally, look at this image from a patient trial. Here, AI-assisted technology predicts a patient’s need for emergency treatment by identifying early signs of disease. By analysing patterns in real-time data, we enabled earlier interventions, reducing hospitalisations and saving lives.

These examples—spanning prenatal care, predictive analytics, decision support, and emergency prevention—demonstrate how AI is transforming medical devices. Each product is built to adapt automatically to the patient, ensuring precision and scalability. This is the future of healthcare, and we’re proud to lead the way.

As I wrap up, I want to leave you with a vision of what’s possible. This image of an enhanced CT lung scan shows how AI, using techniques like mini-batch gradient descent, improves clarity by minimising reconstruction errors. It’s a small but powerful example of how technology can enhance clinical decision-making.

At Camgenium we’re committed to pushing the boundaries of medical technology. From personalised AI models to edge-enabled devices, we’re reducing barriers and accelerating innovation. I invite you to visit our website shown on the next slide to learn more and explore partnership opportunities.