Improving the efficiency of medical image analysis with AI at the edge

The problem: slow and expensive medical image analysis

Every day, pathologists and researchers spend thousands of hours screening images for abnormalities or cells that could be signs of a disease such as cancer. The time of these specialists is not only a very expensive resource, but this process also creates a time latency to provide a diagnosis for the physician and the patient. The current solution to this issue requires sending large images to data centers, where the images are tiled into thousands of smaller images and then inspected by powerful servers, and the data stored in their data lakes. This process is also problematic since it involves an additional latency to transmit the data and involves extra costs for data storage and spinning up and taking down costly GPU server instances in “The Cloud” to perform each of the computational steps.

A new approach to medical image analysis is emerging, aiming to establish the foundation for how clinical decisions are made across the healthcare industry. One such platform enables the detection and identification of anomalous cells with speed and precision, empowering healthcare institutions to improve diagnosis and deliver more focused treatments. While the platform delivers results efficiently through its secure cloud-based system, some institutions may opt to incorporate new server technology for on-premise AI inference to further reduce latency, strengthen data privacy, or manage long-term cloud-related costs. These options can provide additional value depending on institutional needs and operational preferences.

A more efficient solution

A new solution being proposed to solve the problem of slow and expensive medical diagnostic imaging is the addition of an inference server with high performance AI Inference modules that are currently being adopted for high volume security camera operations which can now be  adapted for on-premise operations in hospitals and diagnostic centers. 

These servers, which use less than 20% of the power of a GPU server, can be co-located near the diagnostic imaging microscope to avoid the issues of transmission and costs associated with data ingress/egress and cloud storage. This solution, which costs less than 20% of a GPU server, can significantly shorten the time for a diagnosis from hours or even days to less than 30 minutes. 

By reducing the latency of sending huge files over the internet, it allows a simpler path by copying them to a local server. This also affords the luxury of a large memory capacity server and an ultrafast NVMe Solid State Drive storage device, which can tile 100Kx100K pixel images into smaller 640×640 pixel frames and process them on the Inference AI modules through 32 lanes of PCIe bandwidth directly to memory, which significantly reduces the time for computation. It also can be used by a diagnostician to directly interface with the server through a VPN (Virtual Private Network) to securely view the images on the server and see if there are anomalous cells (highlighted by red Yolo squares) or an all-clear signal for a healthy image.

Increasing accessibility through intuitive user interface

In addition to dramatically reducing the latency and cost of medical diagnosis, this product also makes it easy for a physician to see the result. The addition of a custom built User Interface (UI) dedicated to medical health specialists takes this from a device to a complete solution. Through the addition of a state of the art visual agent,  this solution can provide an intuitive presentation of  the information that allows access and navigation through the images managed for human beings.

Conclusion

In conclusion, when companies with disparate technological core competencies (like EOVIsion.ai, GenUI, and Unigen) work together using the latest tools towards a common cause, the result can be much greater than the sum of its parts. Our goal isn’t simply about building the latest and greatest technology. We are passionate about designing solutions that can impact people’s everyday lives. By using our combined technology, we are not just improving the efficiency of medical diagnostic imaging. We’re making sure every patient can get the timely and accurate diagnosis they deserve.

About the author

Jonathan Alexander Brown is a mathematician and risk expert with over a decade of experience analyzing and pricing uncertainty in the global insurance markets. He holds a master’s degree in applied mathematics from the University of Washington. Today, he applies his expertise in managing risk and complex contracts to transform healthcare diagnostics—using AI to reduce diagnostic errors and delays while minimizing the need for second opinions. His work bridges actuarial science and clinical care to scale medical expertise through intelligent systems.

Article Topics

AI at the edge  |  AI/ML  |  diagnostic imaging  |  edge AI  |  edge computing  |  healthcare AI  |  medical image analysis  |  medical inference servers  |  Unigen