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Rayvolve has been recognized by the National Institute for Health and Care Excellence (NICE) as a key AI technology to help healthcare professionals diagnose bone fractures in X-rays across the NHS. The AI solution encompasses four verticals: AZtrauma, AZchest, AZmeasure, and AZboneage. Among these, AZtrauma specifically focuses on detecting fractures, dislocations, and joint effusions. Clinically validated, the solution has demonstrated its ability to tackle one of the biggest challenges in medical imaging—ensuring fractures are diagnosed quickly and accurately, especially during time-pressured workloads in busy clinical environments.

AZmed, a leading company in AI for medical imaging, has partnered with Healthinc, a key distributor of radiology technology in Australia and New Zealand. This collaboration aims to advance the adoption of AI-driven radiology solutions throughout the region.

European MedTech startup AZmed has received 510(k) clearance from the US FDA for its Rayvolve solution that detects fractures on pediatric X-rays. The clearance comes two years after receiving FDA clearance for adult fracture detection. The clearance was supported by an independent study conducted with SimonMed Imaging, one of the largest outpatient imaging providers in the US.

At AZ Sint-Blasius in Dendermonde, they are unpacking an AI tool for bone fractures. The radiologists can now see what artificial intelligence thinks of each scan. This allows them to detect fractures much faster.  After a one-year trial period, it appears that the error rate is very small and that the system does not make more mistakes than humans. It has already been tested on about 250 patients a week. The hospital will continue to use the AI tool.

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The emergency physicians and radiologists at AZ Sint-Blasius have been using an artificial intelligence (AI) application since the beginning of this year that allows them to make faster diagnoses based on RX images.

The AI application is a product of AZmed, the European leader in AI applications for medical imaging. The application was designed to quickly and accurately detect fractures, dislocations and swelling in joints on X-ray images. The instrument is CE-certified as a medical device in Europe and Food & Drugs Administration-cleared in the US.

Since this year, the radiologists and emergency physicians at Maria Middelares have been using an innovative AI application to more quickly diagnose patients with fractures, dislocations and swelling of joints.

The Rayvolve Trauma tool from AZmed, European leader in radiological AI solutions, is a diagnostic AI tool specifically designed to identify different types of abnormalities in standard radiography. The tool is FDA* approved and CE certified* as a medical AI tool.

Ce mardi 20 février, Julien Vidal, président d'AZmed, s'est penché sur la présentation de son entreprise, ainsi que sur leur levée de fonds de 15 millions d'euros pour façonner l'avenir de l'imagerie médicale grâce à l'IA, dans l'émission Tech & Co, la quotidienne, présentée par François Sorel. Tech & Co est à voir ou écouter du lundi au jeudi sur BFM Business.

AI in radiology holds several benefits for radiologists and hospitals in general. Learn all that AI offers for radiology in this blog and how you can optimize X-ray diagnostics in your healthcare center.

A commercially available AI algorithm that can prioritize x-ray exams when it detects fractures yields “tremendous reductions” in report turnaround times, according to a study presented November 28 at RSNA.

Sean Raj, MD, discussed a pilot study led by outpatient radiology group SimonMed Imaging that evaluated by AZmed on turnaround time (TAT) for fracture detection. Based on the pilot study, the group plans to fully implement the software by the end of the year, Raj noted.

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Julien Vidal, 28, Alexandre Attia, 28, and Elie Zerbib-Attal, 28, the cofounders of AZmed on a mission to “augment doctors with the power of AI", are now nominated as part of the 2022 cohort of Forbes 30 Under 30

Today, it is universally accepted that Deep Learning algorithms are capable of achieving excellent performance in a known environment

Inferencing after conditional training involves a specific procedure which I'll let you inquisitive souls uncover for yourselves from this study [15] There you have it, an A.I solution for pneumonia cause classification. Medical workers equipped with an A.I solution as such would be able to make a rapid and reliable diagnosis - two r's that are essential to compensate for the lack of medical workers.

Today, it is universally accepted that Deep Learning algorithms are capable of achieving excellent performance in a known environment.

Technological innovations are only disruptive when we are not well prepared. In order to accommodate these innovations without disrupting doctors' habits, the steps taken in terms of data protection are therefore essential prerequisites, before considering any collaboration with healthcare institutions

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