The new model is designed as a software component for developers building applications for radiology reporting and image-based workflows.
HOPPR has introduced its MC Chest Radiography Narrative Model, a vision language model designed to translate chest X-ray images into descriptive, structured text. According to a company announcement, the model is offered as a foundational software component that developers can integrate into their own applications to support radiology reporting and workflow solutions.
The model processes standard frontal and lateral chest X-rays, interpreting visual patterns to generate corresponding text. HOPPR notes that the model was trained on a large set of chest X-ray reports to cover a wide range of common patterns, and that it maintains records of training data to support traceability and downstream evaluation by developers. It also includes version control to ensure consistency across development and deployment.
“The industry is in an arms race where a state-of-the-art model or point solution is outdated in weeks,” says Khan Siddiqui, MD, co-founder and CEO of HOPPR, in a release. “What matters is flexible, underlying infrastructure that teams can adapt to their specific environment. This model is built with flexibility in mind: it’s a component that organizations can shape to their workflows and data with the traceability and validation behind it to support responsible deployment.”
The model is intended for organizations developing AI-powered radiology capabilities and is deployed with support from HOPPR’s Forward Deployed Services team. This approach allows organizations to evaluate and adapt the model for their specific use cases, workflows, and data environments, according to the company.
“We just gave medical images a voice,” says Roger Boodoo, MD, medical director of AI at HOPPR and a practicing radiologist, in a release. “For over a decade, AI gave us a second set of eyes but left us to do all the talking. By providing models that enable developers to build applications that translate images into natural language workflows, we’re reducing AI friction and giving radiologists their time back.”
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