HOPPR has introduced a vision-language model designed to translate 2D mammography images into narrative language describing imaging characteristics.
HOPPR EB 2D Mammo Narrative Model is intended as a foundational software component for developers building AI-assisted breast imaging and radiology workflow applications, the company said. Specifically, the model generates structured JavaScript Object Notation (JSON) output from standard 2D mammography images for integration into downstream radiology workflow applications, according to the firm.
The model was trained on more than 200,000 mammography studies from multiple U.S. sites, covering varied breast density categories and implant-displaced imaging scenarios, and includes version control to allow developers to lock specific versions for consistency across application development and updates, HOPPR added.
![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)
