Deep Learning-
Based Tools to
Improve Radiology Workflows




contextflow SEARCH uses deep learning plus 3D medical imaging to put the knowledge encoded in thousands of medical images and reports at radiologists’ fingertips. It is designed to decrease the time spent searching for case-relevant information during image interpretation while increasing confidence and reporting quality.


Simply mark a region of interest in an image, and SEARCH instantly returns reference cases and associated knowledge plus medical literature necessary for differential diagnosis…within seconds. No more paging through books, guessing the right keyword or consulting various text-based reference search engines. Best of all, contextflow SEARCH integrates directly into your PACS.


Currently, contextflow SEARCH is being used to search for 19 different diease patterns in lung CTs, including those related to COVID-19. Results of our first clinical study of contextflow SEARCH Lung CT in collaboration with the Medical University of Vienna (MUW) and Vienna General Hospital (AKH) will be published soon.

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contextflow TRIAGE draws your attention to where it’s needed most: time-critical patients. Once a scan is processed, contextflow TRIAGE identifies patients with known disease patterns present, allowing you to reorder your worklist and prioritize these individuals while reducing any backlogs. The settings are customizable, enabling radiologists to remain in the driver’s seat throughout the entire diagnostic process. And just like contextflow SEARCH, contextflow TRIAGE integrates directly into your RIS/PACS.

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Global Radiology


Increasing Workload


Radiologist Shortage


Increasing Complexity

Proof of concept

contextflow is currently in its proof of concept phase, testing its software with 10+ international partner hospitals and clinics.


Additional information coming soon.



After using and advising several radiology AI software companies, I can say that what contextflow offers is actually the next generation of AI products to support the radiologist, not replace them. Their general approach means they recognize all relevant findings, not just one.

Anand Patel

MD, Chief of Interventional Radiology, Providence Little Company of Mary Medical Centers


We’re very interested in using AI to improve the hospital experience for both doctors and patients; contextflow’s use of deep learning, particularly for lung diseases, is exactly the type of technology we want to evaluate. I very much look forward to the results.

Christian Herold

Head of Radiology at Vienna General Hospital


At Dubrava University Hospital, we take pride in providing the best care possible to our patients. There are many AI radiology solutions, but we agreed to the proof of concept with contextflow because their solution provides real value, particularly for new residents.

Boris Brkljacic

President of the European Society of Radiology


We are very interested in using tools based on artificial intelligence like contextflow SEARCH to support the decision in the diagnostic process based on the image.

Lluís Donoso Bach

President of the International Society of Radiology


Markus Holzer

AI in radiology will increasingly be adopted in clinical routine, augmenting radiologists in mundane and simple tasks as well as providing them with all the relevant context to enable fast and high quality reporting

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