Qure.ai, a provider of artificial intelligence (AI) software for medical imaging, has been awarded the CE Class IIb certification for medical devices under the European Union Medical Device Regulation (Regulation (EU) 2017/745) (EU-MDR).
rawf8 Shutterstock
1131501338
European approval
Qure.ai now offers a portfolio of AI-based detection devices for radiology in the EU, spanning over 31 countries. Qure’s AI helps detect abnormalities from multiple radiology modalities including Chest X-rays, Chest CT, Head CT, and Musculoskeletal X-rays. These medical devices are AI software that assist radiologists in interpreting radiological scans in different healthcare settings.
Prashant Warier, co-founder and CEO of Qure.ai, said: "The EU MDR certification demonstrates our commitment to meeting the highest standards of safety and effectiveness of our products. Qure's products have undergone extensive validation and have been actively deployed globally at more than 1000 sites across 75+. Receiving EU MDR certification is a testament to the continued quality of our solutions, and it further strengthens our position as a trusted provider of AI software. The EU MDR certification will allow us to expand our reach in the EU market and enable better patient outcomes with our imaging AI solutions. We will continue to work closely with regulatory bodies to ensure compliance with the EU MDR and other relevant regulations."
EU MDR certification ensures that medical devices meet high safety, performance, and quality standards. Qure products are validated in 71 countries.
Dr. Jacob J. Visser, radiologist, chief medical information officer, and assistant-professor Value-based imaging AI at Erasmus MC, said: "Last year, Erasmus MC and Qure.ai launched an AI Innovation Centre for Medical Imaging. This innovation centre will evaluate Qure's solutions for their robustness and ability to improve patient outcomes. Gauging the performance of AI solutions in different population cohorts will help create evidence for their adoption in care workflows as clinical decision support tools. This will give detailed insights into their use cases in disease-specific workflows like lung cancer, ER, stroke, etc., and assess the added value for patient care.”
