HOPPR has released its MC CXR Narrative Model, a vision-language model that translates chest x-ray images into descriptive, structured text for use in radiology reporting workflows.
The model processes standard frontal and lateral chest x-rays and generates narrative language that developers can integrate into downstream reporting and workflow applications, according to the company. It features version control to support consistency across development and deployment, and training data traceability to support transparency, HOPPR said.
MC CXR is deployed with support from HOPPR's Forward Deployed Services team, which works with partners to evaluate and adapt the model to specific use cases, workflows, and data environments, according to the firm.
![Representative example of a 16-year-old male patient with underlying X-linked adrenoleukodystrophy. (A, B) Paired anteroposterior (AP) chest radiograph and dual-energy x-ray absorptiometry (DXA) report shows lumbar spine (L1 through L4) areal bone mineral density (BMD). The DXA report was reformatted for anonymization and improved readability. The patient had low BMD (Z score ≤ −2.0). (C) Model (chest radiography [CXR]–BMD) output shows the predicted raw BMD and Z score in comparison with the DXA reference standard, together with interpretability analyses using Shapley additive explanations (SHAP) and gradient-weighted class activation maps. The patient was classified as having low BMD, consistent with the reference standard. AM = age-matched, DEXA = dual-energy x-ray absorptiometry, RM2 = room 2, SNUH = Seoul National University Hospital, YA = young adult.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/04/ai-children-bone-density.0snnf2EJjr.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
