Emergency Room

qXR by Qure.ai

First CE marked AI-Based Chest X-ray Interpretation Tool

Automatic Screening of Chest-X-Ray Abnormalities

With 4 millions exams performed every year in France alone, chest X-Ray is the most common radiologic examination (1). Frequently performed in the emergency department, facing a critical shortage of radiologists, scan interpretation remains challenging as various anatomic structures can overlap in a single 2-dimensional image. It requires experience and expertise and is prone to errors (2) (accounting for 22% of all errors in diagnostic radiology (3)).

What if an AI solution could detect chest abnormalities on thorax radiography?

Trained on 2.3 millions images, qXR (4) serves as a radiology assistant to augment the fast and accurate detection of thorax abnormalities and thus help radiologists evolving in a highly constrained environment to optimize hospital workflow and patient classification.

qXR can screen a chest X-ray and distinguish between normal and abnormal scans, as well as detect and localize various abnormalities including lung parenchymal opacities, pneumothorax, pleural effusion, cardiac enlargement, and anatomical variations seen in the chest with an accuracy equivalent to the radiologists (5).
Pre-populated reports are generated with relevant findings and localization.

Expected benefits

  • Streamlining your workflow by prioritizing abnormal studies in the worklist.
  • Increasing your confidence in chest X-Ray interpretation.
  • Decreasing time to generate reports by pre-populating them with findings.
  • Integrating a decision support tool into your workflow – without slowing you down.


  1. United Nations Scientific Committee on the Effects of Atomic Radiation.Sources and Effects of Ionizing Radiation: UNSCEAR 2008 Report. Vol 1. New York, NY: United Nations; 2010.
  2. Forrest JV, Friedman PJ. Radiologic errors in patients with lung cancer. West J Med. 1981; 134:485– 490. PMID: 7257363
  3. Donald J, Barnard SA.Common patterns in 558 diagnostic Radiology Errors.J Med Imaging Radi at Oncol.2012 ; 56(2):173-178. doi:10.1111/j.1754-9485.2012.02348.x
  4. Can Artificial Intelligence Reliably Report Chest X-Rays? Radiologist Validation of an Algorithm trained on 2.3 Million X-Rays. arXiv:1807.07455v2 [cs.CV] 4 Jun 2019
  5. Deep learning in chest radiography: Detection of findings and presence of change. Ramandeep SinghID1,2, Mannudeep K. Kalra1,2, Chayanin NitiwarangkulID1,2,3, John A. Patti1,2, Fatemeh Homayounieh1,2, Atul Padole1,2, Pooja Rao4, Preetham Putha4, Victorine V. Muse1,2, Amita Sharma1,2, Subba R. Digumarthy . PLOS ONE | https://doi.org/10.1371/journal.pone.0204155 October 4, 2018