New York-based RADLogics and the Moscow Center for Diagnostics & Telemedicine shared the results of a large-scale study (Moscow Experiment on the Computer Vision for the Analysis of Medical Images –, NCT04489992) conducted by the Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department. The clinical research found that the introduction of RADLogics’ AI-Powered solution into radiology workflow to analyze Chest-CT scans during the COVID-19 pandemic reduced report turnaround time by an average of 30%, which is equivalent to 7 minutes per case.

Presented by Tatiana Logunova, MD, of the Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department during the recent ECR 2021 conference, the extensive research included a total of 128,350 Chest-CT scans, of which 36,358 were processed by RADLogics AI-Powered COVID-19 solution, reported by 570 participating radiologists at over 130 hospitals and outpatient clinics in Moscow.

“Early on in the pandemic, it was clear to us that COVID-19 required new healthcare management approaches, and effective clinical management depends more on disease severity than on the virus identification,” says Sergey Morozov, MD, PhD, MPH, who serves as CEO of Moscow Diagnostics and Telemedicine Center. “As a result, the aim of our research was to determine the impact of the introduction of AI-services analyzing Chest-CTs for COVID-19 related findings on the radiologists’ workflow and performance. In addition to finding that the integration of AI did not have a negative effect on the interpretation or report accuracy, our researchers found a significant improvement in productivity and report turnaround time by the expert radiologists that leveraged AI.” 

The study was conducted over two separate phases with the first taking place between April 30, 2020 and June 18, 2020 and the second taking place between June 18, 2020 and August 31, 2020. The study found that report turnaround time was significantly shorter for all time periods in a group of radiologists with available AI results that were seamlessly integrated into radiologists’ current workflow, compared to a group with non-available AI results. In addition, in the shift between the two study time periods, additional clinical parameters were added to the standard of care, including the addition of a disease severity score. The added information created an increased workload on radiologists, which increased the average read time by more than 25%. In response, the RADLogics AI-Powered COVID-19 solution was enhanced to support the new clinical requirements. Results shown indicate that with the augmented AI solution, including all clinical measurements and severity scoring, was able to maintain the overall productivity gain of 30%.

“We applaud this significant real-world research by Dr. Morozov and his team, who were on the frontline of Moscow’s successful fight against the COVID-19 pandemic while demonstrating the value of embracing new AI technologies to aid in these efforts,” says Moshe Becker, CEO and co-founder of RADLogics. “This study—first of its kind in its scale—demonstrates the full potential of AI as a tool to augment radiologists to increase throughput, improve efficiency, and reduce time-to-treatment. This research provides large-scale clinical validation to an earlier academic study by UCLA that was published in Academic Radiology, which conducted a time-motion study using our AI-powered solution to measure the impact of our solution on radiologists’ productivity that found out using our solution saved up to 44% in radiologists’ reading time.”

Dr. Morozov’s research team from the Moscow Center for Diagnostics & Telemedicine Center included Drs. T. Logunova, A. E. Andreychenko, V. Klyashtorny, K. M. Arzamasov, and A. Vladzymyrskyy. The presentation entitled “Artificial intelligence services impact on radiologist’s performance in the context of the COVID-19 pandemic” is available for ECR 2021 registrants.