A study led by Louis Lind Plesner, MD comparing the performance of four commercial AI CXR tools was published in the Radiology Journal, with Oxipit ChestEye among the ones tested. The study evaluated AI algorithm performance for detecting airspace disease, pneumothorax, and pleural effusion.

A study by researchers at Mahajan Imaging and CARPL.ai was conducted to evaluate the performance of diagnostic AI tools for quality assurance. The study found that nearly 1% of chest radiographs included clinically significant diagnostic mistakes, which were identified by Oxipit Quality AI software. The study was presented at the ECR2023 annual meeting earlier in March.

An AI tool can accurately identify normal and abnormal chest X-rays in a clinical setting, according to a study published in Radiology. The tool may greatly alleviate the heavy workload experienced by radiologists globally.  

For the annual European Congress of Radiology meeting in Vienna, Oxipit has conducted a retrospective validation study from internal ChestEye Quality performance data at more than 10 pilot deployment institutions. The study concludes that 1 in 552 (0.18%) chest X-ray studies reported by a radiologist include clinically significant diagnostic mistakes. The vast majority - 78.46% - of the errors are missed findings of pulmonary nodules and lymphadenopathy.

Researchers in Finland released a preprint of a study on the application performance on real-world clinical data. In the study, the application analyzed 10.000 chest X-rays of Finnish primary health care patients. ChestLink software was able to identify normal studies with 99.8% sensitivity and and specificity of 36.4% with a minimal number of false negatives that would lead to effectively no compromise on patient safety and no critical findings missed by the software.