Rad AI will deploy its AI reporting across the imaging network of Yale New Haven Health System's academic medical center in Connecticut.
The network spans more than 16 outpatient imaging centers, five hospital campuses, and more than 700,000 annual radiology exams, the company noted in an announcement. The goal is to relieve administrative burdens and workflow friction, including fragmentation, repetitive speech corrections, and manual data entry, the company added.
The collaboration represents a shift away from treating the radiology report as an isolated administrative task. The announcement coincides with the 2026 annual meeting of the Society for Imaging Informatics (SIIM) in Pittsburgh.
![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)
