New vision-language model translates chest X-rays into descriptive, structured text, giving developers a foundation for radiology workflow applications.

• The HOPPR^™ MC Chest Radiography Narrative Model is a Vision Language Model that translates chest X-rays into structured narrative text for use in radiology reporting workflows.
• The model is designed as a software component for integration into downstream applications, deployed with support from HOPPR's Forward Deployed Services (FDS) team.
• It is built with traceability, validation, and real-world deployment in mind, supporting responsible AI development.

CHICAGO, April 23, 2026 /PRNewswire/ -- HOPPR, a company focused on transforming how AI is developed for medical imaging, today introduced its HOPPR^™ MC Chest Radiography Narrative Model (MC CXR Narrative Model), a vision language model designed to translate chest X-ray images into descriptive, structured text. The model is designed as a foundational software component that developers can integrate into their own applications to support the development of radiology reporting and image-based workflow solutions.

"The industry is in an arms race where a state-of-the-art model or point solution is outdated in weeks," said Khan Siddiqui, MD, co-founder and CEO of HOPPR. "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."

Core capabilities:

• Narrative Language Generation: Translates chest X-ray images into descriptive, structured language that can be adapted for use within radiology reporting workflows.
• Chest Radiography Image Input: Processes standard chest X-rays, including frontal and lateral views, and interprets visual patterns to generate corresponding text.
• Broad Coverage: Trained on a large set of chest X-ray reports and evaluated across a wide range of common patterns, with sampling designed to reflect diverse conditions and variability seen in real-world data.
• Training Data Traceability: HOPPR maintains records of training data to support transparency, traceability, and downstream evaluation by developers.
• Performance: The model has been evaluated against internal benchmarks and demonstrates strong performance with the flexibility to be further fine-tuned to align with the developer's final application.
• Version Control: Allows teams to lock model versions, ensuring consistency and reproducibility across development and deployment.

The model is designed for organizations developing AI-powered radiology capabilities and is deployed with support from HOPPR Forward Deployed Services (FDS). This approach allows organizations to evaluate and adapt the model to their specific use cases, workflows, and data environments, helping ensure alignment with real-world operational needs. FDS works closely with partners to evaluate and implement the model, supporting initial applications across workflow augmentation, training, and research.

"We just gave medical images a voice," said Roger Boodoo, MD, Medical Director of AI at HOPPR and practicing radiologist. "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."

The release of the MC CXR Narrative Model marks a continued expansion of HOPPR's AI development portfolio. To learn more, visit www.hoppr.ai.

About HOPPR
Founded in 2019, HOPPR brings together experts in clinical radiology, AI development, and healthcare commercialization to advance the development of transparent and scalable AI for medical imaging. The HOPPR™ AI Foundry is a secure development platform designed for building, fine-tuning, validating, and hosting AI models for medical imaging. The platform provides curated datasets, traceable development workflows, and secure infrastructure that support responsible AI development aligned with industry quality and regulatory standards. For more information, visit www.hoppr.ai.

Media Contact: Madeleine Bumstead, mbumstead@ampublicrelations.com

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SOURCE HOPPR