Imaging AI software developer HOPPR has integrated two NVIDIA open models into its HOPPR AI Foundry platform for building, refining, and validating medical imaging AI applications, the company announced at NVIDIA GTC 2026.
The first model, NV-Reason, introduces multimodal reasoning capabilities for chest x-ray interpretation workflows, generating structured analytical reasoning steps alongside model outputs to provide greater transparency into how imaging interpretations are produced, according to the company. The second, NV-Generate, is a latent diffusion model that creates high-fidelity synthetic DICOM imaging datasets that support data augmentation and model training where real-world data may be limited, HOPPR said.
The HOPPR AI Foundry is built on NVIDIA A100 and H100 GPUs and supports optimization with NVIDIA TensorRT and inference through NVIDIA Triton Inference Server, the company noted.













![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&fit=crop&h=112&q=70&w=112)





