Advancing Teleradiology with AI: How Hexarad is Innovating with Machine Learning

Artificial intelligence (AI) has led to unprecedented advancement opportunities across sectors, with teleradiology being no exception. But integrating AI into this sector isn’t just about automation, it’s also about equipping highly skilled radiologists with tools that elevate reporting services and improve patient outcomes. At Hexarad, my role as a data scientist and machine learning engineer focuses on supporting this AI integration; building tools to reshape how we support our radiologists and streamline operational workflows. In this article, I’ll dive into some exciting examples of this work and discuss the future of AI at Hexarad.

Generative Mapping Tables: Revolutionising Customer Onboarding 

One of the key areas where AI is making a tangible impact at Hexarad is in the creation of generative mapping tables. Traditionally, onboarding new customers in a teleradiology company requires a significant amount of manual data mapping, ensuring that hospital systems align with our internal processes. This process can be time-consuming and prone to human error. 

Using AI-driven generative models, we have automated much of this process. Our AI models intelligently match hospital and radiology system data with our internal reporting structures, providing a more robust and efficient onboarding experience. This not only enhances operational efficiency but also allows radiologists to start reporting sooner, ensuring that patients receive their diagnostic results without unnecessary delays. 

AI-Powered Insights: Leveraging Large Language Models for Performance Tracking 

At Hexarad, we are also developing AI-powered insight reports. Our AI pipeline summarises vast quantities of historical radiology data as key performance metrics, using generative mapping tables (described above) and Large Language Models (LLMs) to distil key ideas. 

These reports empower hospitals to make data-driven decisions that highlight opportunities for smarter resource allocation and inform future commissioning opportunities. Key trends displayed in the report include: 

• Turnaround times: providing insights into reporting efficiency by scan type, benchmarked against industry standards. 

• Backlog management: visualising shifts in reporting backlogs over time to uncover pressure points in demand. 

• Scanning activity: capturing patterns in imaging requests to confirm anecdotal trends in demand at a daily and weekly level. 

Using these AI-powered reports, hospitals can gain a clearer picture of how their radiology departments are performing over time; enabling positive interventions that ultimately enhance patient care. 

The Future of AI in Teleradiology 

With scan demands increasing in the UK despite widely discussed workforce shortages (with a current 30% shortfall in radiologists expected to rise to 40% by 2028), innovations to improve efficiency in this field are now more important than ever. At Hexarad, we are committed to staying at the forefront of this innovation.  Whether it’s automating customer onboarding with generative mapping tables or using LLMs to deliver meaningful insights, AI is already helping us to support radiologists and healthcare providers; allowing faster, more accurate and more efficient reporting to be achieved. As the capabilities of AI continue to evolve, I am excited to explore further opportunities to incorporate these tools into the work that we do at Hexarad. 

Interested in learning more about how AI is shaping the future of radiology? Get in touch with our team at Hexarad to explore our innovative radiology AI solutions and discover how we can support your hospital’s imaging and reporting needs.