Pathology, Trauma, and Forensic RadiographyJan 17th, 2014FacebookLinkedInX (Twitter)Pinterest Add us as a preferred source on Google San Francisco, CAUShttps://store2.aheconline.com/index.php?route=product/product&path=59_81&product_id=1055This course is designed to improve your overall awareness of the tissue density differences in diseases and trauma cases at various stages.FacebookLinkedInX (Twitter)Pinterest Add us as a preferred source on Google Latest in HomeRadiology dominates thirty years of FDA AI device approvalsJuly 16, 2026Petition seeks to increase number of attempts for taking ARRT examJuly 16, 2026Societies applaud introduction of Patients First ActJuly 16, 20262027 MPFS proposed rule would lift radiology reimbursementJuly 15, 2026
![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.auntminnie.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)
