French radiology artificial intelligence (AI) software developer Gleamer is highlighting a study in Radiology that shows its BoneView software aided in lowering the false-negative rate of undetected fractures by 30%.
The AI algorithm increased sensitivity by 12% and specificity by 5% per patient without reducing reading speed on exams that were chosen due to their difficulty, according to the firm.
BoneView was trained on approximately 60,000 x-rays from trauma patients prior to the study. Researchers obtained x-rays from 600 adults who had trauma with or without one or more fractures of the shoulder, arm, hand, pelvis, leg, or foot between 2016 and 2018 for this study. Then, six radiologists and six emergency room physicians viewed the images with and without BoneView to detect and localize fractures.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
