AI-Based Chest X-Ray Diagnosis Technology Secures CE Mark Approval

The algorithm’s high level of accuracy in identifying normal chest X-rays exams means that the red dot® platform can be used to speed up the detection of those suspected with COVID-19.

behold.ai provides artificial intelligence, through its red dot® cognitive computing platform, to radiology departments. The technology augments the expertise of radiologists to enable them to report with greater clinical accuracy, faster and more safely than they could before and helps to deliver a greater performance in radiology reporting at a fraction of the price of outsourced reporting.

The behold.ai algorithm has been developed using more than 30,000 example images, all of which have been reviewed and reported by highly experienced consultant radiology clinicians in order to shape accurate decision making. The red dot® prioritisation platform is capable of sorting images into normal and abnormal categories in less than 30 seconds post image acquisition.

Recently, behold.ai and Wellbeing Software entered into a collaboration to fast-track the diagnosis of COVID-19 in the UK NHS hospitals using AI analysis of chest X-rays. A national roll-out would enable a large number of hospitals to triage suspected COVID-19 patients inside and outside of the hospital setting using chest X-rays. It is currently being used as the key diagnostic test for triage of COVID-19 patients and is also useful in dealing with the backlog of radiology cases, such as suspected lung cancer patients. behold.ai estimates that its AI-based technology has the potential to make up to GBP 100 million in cost savings for the UK NHS.

“This is a great result for the company and for our team of quality leaders, AI engineers and clinicians who have worked for years to develop a system that meets the highest quality standards,” said Simon Rasalingham, Chairman and CEO of behold.ai. “There is, quite rightly, a big focus on ensuring that the relatively new technology of artificial intelligence produces solutions that have a high degree of accuracy, reliability and scalability. This first in-kind regulatory approval is a key achievement for companies in this sector, the first autonomous AI algorithm to rule out normal chest X-rays.”

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