AI-Rad Companion supports the physicians reading workflow and may increase your diagnostic precision when interpreting medical images

AI-Rad CompanionProviding multi-modality imaging decision support


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The AI-Rad Companion, our family of AI-powered, augmented workflow solutions, helps you to reduce the burden of basic repetitive tasks and may increase your diagnostic precision when interpreting medical images.

Its solutions provide automatic post-processing of imaging datasets through our AI-powered algorithms. The automation of routine workflows with repetitive tasks and high case volumes helps you to ease your daily workflow – so that you can focus on more critical issues.

AI-Rad Companion Confirmation User Interface
This image shows the confirmation screen of the AI-Rad Companion Chest CT

Our state-of-the-art algorithms will be automatically distributed to you as a user as soon as they are officially released and made available. Once the images are post-processed by the AI-Rad Companion, it supports your interpretation of the data by automatically providing results of its analysis to you for review, confirmation and possible inclusion in the final report or care pathway – to raise your precision and ensure high quality outcome in diagnostic decision-making.

All our AI-Rad Companion extensions are deployed via the teamplay digital health platform. This approach eases regular updates as well as upgrade processes and facilitates the integration of new offerings into the existing IT environment.

Learn more about AI-Rad Companion offerings for various modalities and body regions.

How customers use AI-Rad Companion

Customers around the world are using AI-Rad Companion in their clinical routine. Learn how they benefit since they started using AI-Rad Companion Chest CT in their daily work.

Clinical Outcomes

We developed the AI-Rad Companion to help you cope with a growing workload. With its deep learning algorithms, AI-Rad Companion automatically highlights abnormalities, segments anatomies, and compares results to reference values.




The entire global population has been impacted, in one way or another, by the COVID-19 virus. The AI-Rad Companion, our Siemens Healthineers AI driven solution, offers our customers the opportunity to have access to different postprocessing software for the analysis of lung examinations.

The algorithm is designed to automatically identify and quantify abnormal tomographic patterns in the lungs from chest CT for research purposes. The system takes as input a non-contrasted chest CT, identifies and 3D segments the lungs and lobes before segmenting the abnormalities. It outputs two combined measures of the severity of lung/lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities. High opacity abnormalities were shown to correlate with severe symptoms. The first disease severity measure is global, while the second is lobe-wise.

First global measure:

  • Percentage of Opacity (PO): Percentage of predicted volume of abnormalities compared to the total lung volume
  • Percentage of High Opacity (PHO): Percentage of predicted high opacity volume compared to the predicted volume of abnormalities

Second lobe-wise measure: 

  • Lung Severity Score (LSS): The extent of abnormalities across each lobe
  • Lung High Opacity Score (LHOS): The extent of high opacity abnormalities for each lobe

The computed results could be used to analyze the severity and monitor the progression of abnormalities in patients exhibiting COVID-19 symptoms.

The performance of the method in estimating PO, LSS, PHO, and LHOS is evaluated on a database of 100 COVID-19 cases and 100 controls from multiple institutions from Canada, Europe, and the U.S. Ground truth is established by computing the same measures from manual annotations of the lesions, lungs, and lobes.

This prototype is designed to automatically identify and quantify airspace opacities of the lung, enabling simple to use analysis of chest X-rays for research purposes only (not for clinical use). AI-Rad Companion Research Chest X-ray Pneumonia Analysis will perform automated lung opacity analysis on Chest X-ray (AP or PA viewing direction), highlighting identified regions. Other radiographical findings, such as pneumothorax, pulmonary lesions, atelectasis, consolidation and pleural effusion are included as well.

Chest imaging, X-ray in particular, plays an important role in patient management during the COVID-19 pandemic5. Patient management and clinical decisions depend on clinical outcomes and imaging reports. The new member of the AI-Rad Companion family, the AI-Rad Companion Chest X-ray, automatically processes upright chest X-ray images (PA direction). Next to pneumothorax, pleural effusion and nodule detection, the AI-Rad Companion Chest X-ray is able to indicate consolidations and atelectasis. The latter may be signs of pneumonia caused by the COVID-19 virus.