{"id":10174,"date":"2021-12-23T07:24:49","date_gmt":"2021-12-23T06:24:49","guid":{"rendered":"https:\/\/thesmartcityjournal.cibeles.net\/sin-categoria\/artificial-intelligence-helps-doctors-process-over-one-million-covid-19-examinations\/"},"modified":"2021-12-22T19:18:05","modified_gmt":"2021-12-22T18:18:05","slug":"artificial-intelligence-helps-doctors-process-over-one-million-covid-19-examinations","status":"publish","type":"post","link":"https:\/\/www.thesmartcityjournal.com\/en\/smart-health\/artificial-intelligence-helps-doctors-process-over-one-million-covid-19-examinations","title":{"rendered":"Artificial Intelligence Helps Doctors Process over One Million COVID-19 Examinations"},"content":{"rendered":"

Digital services have reduced the average time needed to obtain a medical report to 6.5 minutes<\/em><\/p>\n

Artificial intelligence services continue to help Moscow radiologists identify signs of infection on CT scans. Neural networks have analysed over one million examinations used to diagnose COVID-19 in the past 1.5 years. Also, thanks to digital services, the average time needed to obtain a medical report is 6.5 minutes, while in emergency situations attending doctors receive a radiologist\u2019s protocol within only five minutes after an examination.<\/p>\n

\u201cThe Moscow healthcare system turned out to be prepared for an increased workload in the conditions of growing morbidity. The most useful innovative technology is computer vision, which simplifies doctors\u2019 work when they describe a large number of images. The digitalisation of the entire diagnostic process has significantly influenced the examination time: the entire process \u2013 from sending a CT scan to the service to obtaining a detailed report \u2013 may take only five minutes in emergency cases, while now it takes 6.5 minutes, on average. Today, neural networks have helped Moscow radiologists process over one million examinations of that kind meant to detect COVID-19,\u201d said Natalya Ledikhova, Deputy Director for Medicine at the Diagnostic and Telemedicine Centre of the Moscow Healthcare Department, radiologist.<\/strong><\/p>\n

Last spring, Moscow started taking new approaches to healthcare based on innovative technologies. The reason was a need to reduce the time meant to diagnose COVID-19 and prescribe treatment. The use of artificial intelligence in radiation diagnostics allowed speeding up this process and simultaneously improving its quality. Specialists started using such services to automatically recognise the signs of the coronavirus infection on the CT images of the lungs.<\/p>\n

Computer vision algorithms based on artificial intelligence were used in due time and have also confidently entered medical practice. These algorithms help doctors identify patient groups with a high disease development risk.<\/p>\n

Protocols and images of instrumental examinations (CT, MRI, X-ray, PET\/CT and others) are available to electronic medical record users on mos.ru and in the UMIAS.INFO mobile application. To receive an archive with examination images, file a request in the electronic medical record in the My Examinations section.<\/p>\n

The examinations are performed with sophisticated medical equipment, so the result is a high-quality detailed DICOM image. The archive has a special programme for viewing such images, as well as a user guide. Archive download links will be valid for five days from the date of their receipt.<\/p>\n

Such services became available due to a single digital healthcare platform developed by the Moscow Social Development Department and the Information Technology Department of the city. The platform provides a variety of services for doctors and patients simplifying their interaction and improving the quality of examinations and treatment.<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Digital services have reduced the average time needed to obtain a medical report to 6.5 minutes Artificial intelligence services continue to help Moscow radiologists identify signs of infection on CT scans. Neural networks have analysed over one million examinations used to diagnose COVID-19 in the past 1.5 years. 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