02 October 2025
Technology for the autonomous detection of diabetic retinopathy will be deployed across Norway’s largest health authority.
A statement from AI-powered screening company, Eyenuk, confirmed that the EyeArt AI Eye Screening System will be rolled out across the South-Eastern Norway Regional Health Authority. The authority covers 3.1 million people – more than half the population of Norway.
EyeArt has US Food and Drug Administration approval for the detection of more-than-mild and vision-threatening diabetic retinopathy.
Eyenuk chief executive, Gaurav Agarwal, highlighted that the development was the first deployment of EyeArt within a nationwide health system.
“We are proud that Norway has chosen the EyeArt system as its AI solution for diabetic eye screening. We look forward to helping more Norwegians protect their sight by ensuring they receive timely and effective screenings,” Agarwal said.
South-Eastern Norway Regional Health Authority medical director, Ulrich Spreng, shared his hope that the technology would help clinicians to see more patients.
“With better use of healthcare professionals' time, we expect that the waiting time for retinal examinations will be significantly reduced. A better patient overview will also provide more efficient management and control – and create the foundation for greater equality in treatment,” he said.
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Don Williams06 October 2025
This is an exciting and forward-thinking step by Norway, and one that truly reflects the direction ophthalmic care is evolving towards. The deployment of autonomous AI systems such as EyeArt for diabetic retinopathy screening illustrates how clinically validated algorithms can be safely integrated into mainstream healthcare workflows to deliver scalable and efficient screening at population level.
By leveraging deep-learning-based image analysis capable of detecting more-than-mild and vision-threatening pathology with high sensitivity and specificity, clinicians can redirect their time and expertise towards complex case management and nuanced therapeutic decision-making. This type of deployment also aligns with broader goals in population health management — enhancing service capacity, reducing waiting times, and improving risk-stratification capabilities across large patient cohorts.
Importantly, the integration of autonomous AI into national screening programmes is not about replacing clinicians but about augmenting their capacity. It exemplifies how intelligent diagnostic systems can serve as front-line triage tools, enabling earlier intervention, more equitable access to care, and ultimately better visual outcomes at scale.
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