HOPPR™ AI Foundry Expands Medical Imaging AI With NVIDIA Accelerated Computing and Foundation Models

Announced at NVIDIA GTC 2026, NVIDIA NV-Reason and NV-Generate open models are now available on the HOPPR™ AI Foundry, expanding developer access to advanced reasoning and generative AI capabilities for medical imaging development.

Built on the NVIDIA accelerated computing platform for AI training and inference, the HOPPR™ AI Foundry enables developers to build, evaluate, and fine-tune imaging AI within a secure, HIPAA-compliant development environment.

Key highlights:

• Integrated AI development infrastructure: The HOPPR™ AI Foundry combines accelerated computing, curated datasets, foundation models, fine-tune tooling, and traceable development workflows designed for medical AI imaging development.
• Multimodal reasoning for imaging AI: NV-Reason generates structured, analytical reasoning alongside model outputs, providing greater transparency into how imaging interpretations are produced.
• Synthetic medical imaging generation: NV-Generate creates high-fidelity synthetic DICOM imaging datasets that can support AI model development, training, fine-tuning, and validation workflows.
• Model customization with expert support: Developers can run inference on NVIDIA open models for medical imaging within the Foundry and fine-tune them with support from HOPPR Forward Deployed Services (FDS).

CHICAGO, March 17, 2026 /PRNewswire/ -- HOPPR today announced that NVIDIA open models, NV-Reason and NV-Generate, are now available on the HOPPR™ AI Foundry, expanding developer access to advanced reasoning and generative AI capabilities for medical imaging development. Announced at NVIDIA GTC 2026, the integration brings NVIDIA medical imaging models into HOPPR's secure developer platform for building, refining, validating, and hosting AI models for medical imaging.

The HOPPR™ AI Foundry is built on the NVIDIA accelerated computing platform, enabling developers and researchers to train, evaluate, and fine-tune medical imaging AI using high-performance GPU infrastructure and optimized inference software.

The platform is built on NVIDIA A100 and H100 GPUs for large-scale training and inference of HOPPR's proprietary foundation models and customer-specific fine-tuned models. The infrastructure supports optimization with NVIDIA TensorRT™ and efficient inference performance through NVIDIA Triton™ Inference Server. Medical imaging pipelines leverage MONAI-based transforms to accelerate imaging AI workflows.

This integration enables developers building AI applications to experiment with advanced reasoning and generative AI capabilities within HOPPR's secure, scalable AI development environment.

"The next generation of medical imaging AI will combine multimodal reasoning with the ability to generate high-fidelity clinical data," said David Niewolny, Director of Business Development for Healthcare and Medical at NVIDIA. "Platforms like the HOPPR™ AI Foundry enable developers to train and deploy medical imaging on NVIDIA accelerated computing with the performance and scale required for healthcare innovation."

Developing high-performance medical imaging AI models often requires greater transparency into model outputs and access to large, diverse datasets for training and evaluation. The availability of NVIDIA open models within the HOPPR™ AI Foundry expands the range of foundation models available to developers building AI applications for medical imaging.

"Medical Imaging AI is entering a new era where models can reason about images and generate new clinical data to accelerate application development," said Dr. Khan Siddiqui, CEO and Co-Founder of HOPPR. "The HOPPR™ AI Foundry brings together secure infrastructure, curated datasets, fine-tuning tooling, and advanced AI models to help developers build the next generation of imaging AI applications."

The HOPPR™ AI Foundry integrates secure infrastructure, curated datasets, and traceable AI development workflows designed to support the development of medical imaging AI while maintaining strong data controls, auditability, and compliance alignment.

NVIDIA Open Models Available Within the HOPPR™ AI Foundry:

• NV-Reason introduces multimodal reasoning capabilities designed for chest X-ray interpretation workflows in AI development. Rather than producing only final predictions, like differential diagnosis or follow-up recommendations, the model generates structured analytical reasoning steps alongside model outputs, providing greater transparency into how model interpretations are derived.
   
• NV-Generate, a latent diffusion model, enables the creation of high-fidelity synthetic DICOM imaging datasets. The model generates realistic 3D medical images along with paired segmentation masks and anatomical annotations, supporting data augmentation and the development of medical imaging models where real-world training data may be limited.

Together, these models represent an emerging class of medical imaging AI technologies that combine reasoning and generative capabilities to support the development and evaluation of next-generation imaging AI systems.

For developers building medical imaging AI, this combination of reasoning models and synthetic data generation opens new possibilities for dataset expansion, model transparency, and experimentation across imaging AI development workflows.

The Foundry also supports model fine-tuning through HOPPR's Forward Deployed Services (FDS). With this engagement model, HOPPR machine learning engineers, data scientists, and clinical experts work alongside customer teams to develop and refine AI models for imaging applications.

Find out more about HOPPR online at: 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.

SOURCE HOPPR