detection software
for chest CT
integrated directly
in your PACS

contextflow is your comprehensive chest CT guide


Quantitative and qualitative information for lung cancer, ILD and COPD directly in your viewer

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Comprehensive chest CT insights directly in your native viewer

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Comprehensive quantitative profiling of a patient with every chest CT

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contextflow ADVANCE Chest CT provides radiologists comprehensive computer-aided detection support for suspected lung cancer, ILD and COPD cases. Its main features are designed to help save time and improve reporting quality: nodule detection and quantification, nodule tracking over time, quantitative lung tissue analysis for key image findings, and qualitative analysis of 19 image patterns plus reference cases and differential diagnosis information.

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DETECT / Nodule Detection

Detection & quantification of nodules from within your native viewer. Nodule characterization proven to reduce false positives and flag at-risk patients sooner.

TIMELINE / Nodule Tracking

Understand how your patient’s nodules change over time. Save time preparing for follow ups and multidisciplinary team meetings.

INSIGHTS / Lung Tissue Analysis

Anomaly heatmaps show overall distribution of detected disease patterns. Quantification & individual heatmaps for 7 key image patterns.

MSI / Nodule Malignancy Scoring

Reduce false positive nodule detections while simultaneously detecting lung cancer sooner.*


Automatic detection & prioritization of incidental pulmonary embolism from partner Avicenna

SEARCH / 3D Image Search

Qualitative analysis of 19 image patterns in chest CT. Links to differential diagnosis literature. Retrieval of similar cases to yours from a curated knowledge base.

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Latest Research
Average report reading time reduced 31% in clinical study with the Medical University of Vienna

Publication / Abstracts
contextflow’s scientific roots as a spinoff of the Medical University of Vienna means peer-reviewed research forms the foundation of everything we develop.



I have been following contextflow’s progress practically since the company’s founding, and their traction in the area of chest CT is impressive. Being able to shape clinical decision support tools that myself and colleagues can benefit from in clinical practice is a big motivator. We’re literally shaping the future.

Jacob Visser

Chief Medical Information Officer & Head of Imaging IT and Value-Based Imaging at Erasmus MC

I really like the transparency of contextflow as opposed to other black box AI solutions. It’s designed to support my workflow while leaving the final decision up to me.

Elmar Kotter

Vice Chair and Head of Imaging Informatics at the Department of Radiology at Freiburg University Medical Center

In the world of AI, it’s crucial to use it safely, be clear about what it does, and make ethical choices. This means moving forward with innovation in a responsible way, creating a future that’s both advanced and thoughtful.

Geraldine Dean

MD MSc MRCS FRCR Consultant Radiologist, Artificial Intelligence Lead NHS SW London Imaging Network

For interstitial lung disease, we use contextflow on a daily clinical basis. We now put the major information into the radiology report. And this is what our clinicians expect from us: to be able to quantify the disease and especially to quantify disease progression in order to improve clinical decision making.

Gerlig Widmann

Managing Senior Physician at the University Department of Radiology at the Medical University of Innsbruck

One of the great features of contextflow is the TIMELINE view, which offers the possibility to actually analyse follow-up scans. And that has a lot of value for our clinical practice because patients will return to our practice for follow-up imaging.

Willem Grootjans

Head of the Imaging Services Group at the Department of Radiology, Leiden University Medical Center

I use contextflow in any routine scan performed, for example, for staging, or for other disease evaluation. It helps me a lot to recognize patterns in patients where you’d not expect or where we cannot clearly see the pathology behind it. So it helps us a lot as a double checker.

Lukas Müller

Radiology Resident & Clinician Scientist at the Medical University of Mainz

contextflow is one of the applications that certainly fits radiology’s current needs and can simplify the analysis of complex lung pathology. With the right insights and technology, we can succeed in introducing AI in a very attractive way to radiology departments on a global scale.

Erik Ranschaert

Former President of the European Society of Medical Imaging Informatics (EuSoMII), Radiologist at St. Nikolaus Hospital in Eupen


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


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 are very interested in using tools based on artificial intelligence like contextflow 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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View our current openings here.


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