Assist clinicians interpreting chest X-ray studies by detecting up to 124 findings
Assist clinicians interpreting chest X-ray studies by detecting up to 124 findings.
Chest X-ray/DICOM
Indication of presence of finding, segmentation overlay for selected findings, confidence bar.
When fixed at a sensitivity comparable to radiology reports (87.2%), the AI produced critical miss rates of 2.2%, compared to 1.1% for radiologists. At higher sensitivities (≥95.4%), the AI reduced critical misses to ≤1.1%, equaling or outperforming radiologists in detecting critical conditions without increasing the risk of missed diagnoses
In a study of 1961 chest radiographs, the system achieved a specificity of 24.5%, 47.1%, and 52.7% at sensitivities of 99.9%, 99.0%, and 98.0%, respectively. This allowed the AI to correctly exclude pathology in 19.6% of cases at the lowest sensitivity level, thus offering significant time savings for radiologists without increasing the risk of critical misses
In a study involving 2972 chest radiographs, the product contributed to significant report changes in 3.1% of cases, while changes in patient management occurred in 1.4% of cases. Furthermore, additional imaging was recommended in 1.0% of cases due to AI findings.
Out of 2972 cases, the AI model findings aligned with radiologist interpretations in 86.5% of cases, with significant findings affecting patient management in 1.4% of instances.
https://annalise.ai/clinical-evidence/
