Published clinical studies and independent evaluations. Here's the evidence behind xAID's accuracy
A retrospective single-center study evaluated xAID Chest CT on 90 unenhanced chest CT scans in an emergency radiology setting. Pathologies assessed included lung nodules, opacifications, coronary calcification, aortic and pulmonary measurements, pleural and pericardial effusions, pneumothorax, and rib and vertebral fractures
AI outperformed radiologists in detection of coronary artery calcifications, pulmonary artery dilatation, and vertebral fractures
A multi-center retrospective clinical utility assessment conducted across four European radiology centers (France, Greece, Slovakia, United Kingdom) evaluated xAID on 81 non-contrast chest CT cases with four board-certified radiologists
94.9% of radiologists agreed AI-generated structured report elements could be integrated into clinical practice with minor modifications
xAID's core AI is a Swin transformer-based foundation model trained on large-scale clinical CT data. Input resolution up to 256³ voxels captures fine-grained anatomical structure across head, chest, and abdomen
Multiple layers of verification — AI and human — before any report is delivered
A Swin transformer-based foundation model trained on clinical CT data analyzes the study. Input resolution up to 256³ voxels — capturing fine-grained anatomical detail
A second AI layer independently reviews the findings. Divergences between layers are flagged for radiologist attention
Every report is reviewed by a European radiologist before delivery. AI-assisted, not autonomous
95% accuracy verified by peer-reviewed studies. Every report is reviewed by a radiologist before delivery
xAID achieves 95% accuracy verified by peer-reviewed studies. Published clinical evidence from independent research institutions confirms the accuracy of AI-assisted CT reporting with radiologist review
Two independent peer-reviewed studies validate xAID's accuracy claims. Traditional teleradiology and narrow AI overlays offer no equivalent published clinical evidence
Start a free 5-study pilotIn a peer-reviewed study published in Polish Radiology (2025), xAID achieved 92.2% pooled sensitivity and 95.6% specificity across 9 pathology categories in emergency chest CT — compared to 58.3% and 80.6% respectively for radiologists reviewing scans without AI assistance
Published evidence suggests AI-assisted reading reduces missed findings rather than increasing them. In the Polish Radiology study, AI outperformed unaided radiologists in detecting coronary calcifications, pulmonary artery dilatation, and vertebral fractures. Every xAID report also receives radiologist review before delivery
xAID achieves 95% accuracy verified by peer-reviewed studies. In a published study in Polish Radiology (2025), xAID achieved 92.2% sensitivity and 95.6% specificity. A separate multi-center European study found 94.9% of radiologists approved AI-generated report elements for clinical use
xAID's core model is a Swin transformer-based foundation model with input resolution up to 256³ voxels. A second AI verification layer independently reviews findings before radiologist sign-off. The combined approach achieves a macro F1 score of 0.86 across clinically relevant pathologies
Yes. The sensitivity/specificity figures come from a single-center retrospective study published in Polish Radiology (an independent peer-reviewed journal). The clinical utility data comes from a multi-center study across four European radiology centers. Neither was conducted by xAID internally