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Глобальный охват, Филиалы и Установки

Облегчение бремени, лежащего на системах здравоохранения

Обзор

Мы сотрудничаем и сотрудничаем с крупными технологическими партнерами и партнерами-поставщиками OEM — включая Nuance и другие на ключевых рынках — с глобальным охватом, чтобы облегчить быстрое развертывание нашего решения.

ОТЗЫВЫ ПАРТНЕРОВ И КЛИЕНТОВ

“There is significant potential clinical impact in the application of AI to medical images to follow and predict trajectory of disease. Some of the early research that I did in collaboration with RADLogics was in the rapid utilization of machine learning to find and quantify disease in thoracic CT scans of patients with COVID-19. In order to streamline a process that normally might take years to just a few weeks, it was critical to apply transfer learning to existing tools and software to rapidly develop these algorithms.”

Dr. Eliot Siegel, Professor and Vice Chair at the University of Maryland School of Medicine, Department of Diagnostic Radiology and Chief of Radiology and Nuclear Medicine for the Veterans Affairs Maryland Healthcare System

“Current epidemics are calling for new healthcare management approaches, and effective clinical management depends more on disease severity than on the virus identification. We have successfully integrated RADLogics’ AI-powered solution across our hospital network throughout Moscow where imaging plays a crucial role in patient management – specifically chest CT. It allows defining symptomatic patients and stratifying them into mild, moderate, and severe disease burden groups. This clinical risk assessment is greatly supporting decisions on treatment at home, at the hospital, or at the ICU – especially when PCR results are pending or repeatedly false-negative.”

Dr. Sergey Morozov, MD, PhD, MPH, who serves as CEO of Moscow Diagnostics and Telemedicine Center

“The ability of medical imaging – in combination with AI – to discover and quantify the burden of COVID-19 has been well documented. There would be tremendous clinical value in an AI algorithm that could establish and utilize a trajectory of change to predict which subset of patients might need more intense therapy such as mechanical ventilation, which subset of patients could be more confidently discharged, and predict subsequent clinical course. Today, there are many promising AI applications that have emerged that could allow us to address the major challenges that have hit the healthcare sector during the pandemic, and beyond as we plan to treat patients with COVID-related complications and as we provide diagnostic and therapeutic procedures that were delayed during the surge.”

Dr. Eliot Siegel, Professor and Vice Chair at the University of Maryland School of Medicine, Department of Diagnostic Radiology and Chief of Radiology and Nuclear Medicine for the Veterans Affairs Maryland Healthcare System