Accelerating Medical AI Development
Accelerate the development of medical AI applications to streamline clinical workflows and drive innovation.
Build AI Applications for Medical Imaging With NVIDIA Platforms
MONAI
Develop Medical Imaging AI Applications With Open-Source Tools
MONAI is an open-source, domain-specific framework for developing, training, and deploying deep learning models for medical imaging.
Researchers, data scientists, and application developers focused on advancing medical AI can use MONAI to build and refine multimodal algorithms and models for medical applications.
MONAI simplifies the integration and management of advanced AI workflows and provides tools for data labeling, model training, and application development and deployment, effectively standardizing AI lifecycles.
NVIDIA NIM Microservices
Optimize Medical Imaging AI Development With Advanced Models
NVIDIA NIM™ microservices for medical imaging are easy-to-use, GPU-optimized inference services designed to streamline the development of medical AI applications.
Designed to bridge the gap between AI development and production, these microservices offer developers, researchers, and data scientists pre-optimized models and industry-standard APIs to build powerful AI applications.
NIM microservices help accelerate the adoption of advanced AI technologies across medical and biopharma domains by providing a full-stack approach while maintaining high performance and ensuring data security and compliance.
Additional Technologies
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MONAI Training Toolkit and Documentation
NVIDIA’s MONAI toolkit is a development sandbox offered as part of NVIDIA AI Enterprise. It includes a base container and a curated library of 15+ pretrained models, including CT, MR, pathology, and endoscopy, available on NVIDIA NGC™, that allow data scientists and clinical researchers to jump-start AI development.
Platform Updates
Get notified of new releases, bug fixes, critical security updates, and more for medical imaging.
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Got questions? Our healthcare experts are here to assist you!
FAQ
NVIDIA plays a pivotal role in advancing medical imaging reconstruction. As the leading computer provider, NVIDIA provides technology that is integral to the operations of major companies like Siemens, GE Healthcare, Philips, and United Imaging Healthcare.
These companies leverage NVIDIA's powerful GPUs and software toolkits, including NVIDIA® CUDA®, TensorRT™, and Triton™, to enhance the performance of their MRI, CT, and ultrasound systems. NVIDIA's AI and accelerated computing solutions significantly reduce the time required for image reconstruction, thereby improving clinical efficiency and image quality. United Imaging Healthcare, for example, has utilized NVIDIA's technology to develop AI-enabled MR scanners that reduce patient time in MR machines and increase access to MR procedures. They achieved 10x acceleration in computational speed for MR image reconstruction and a 95% reduction in MR image reconstruction time.
MONAI (Medical Open Network for AI) is an open-source framework founded by NVIDIA in collaboration with King's College London and other leading academic medical centers. It aims to establish an inclusive community of AI researchers to develop and exchange best practices for AI in healthcare imaging. Built on top of PyTorch, MONAI provides domain-optimized tools and libraries for developing, training, and deploying AI models in medical imaging applications, such as image segmentation, classification, and registration.
The suite of libraries, tools, and SDKs within MONAI includes:
- MONAI Core: A domain-specific framework for training AI models for healthcare imaging
- MONAI Label: An intelligent image labeling and learning tool to quickly annotate new datasets
- MONAI Deploy App SDK: A framework and associated tools that enable developers to take an AI model and turn it into an AI application
- MONAI Model Zoo: A collection of medical imaging models in the MONAI Bundle format
MONAI benefits medical imaging research by providing a comprehensive set of tools that accelerate the development and deployment of AI models. It includes advanced data preprocessing, neural network architectures, and evaluation metrics tailored for medical imaging. MONAI simplifies the integration of AI into research workflows, enabling faster prototyping, reproducible research, and collaboration across institutions. This leads to improved accuracy and efficiency in medical imaging tasks.
MONAI Multimodal is an open-source toolkit of foundation models, reference workflows, and interoperable building blocks for enabling multimodal analysis of diverse healthcare data—from CT and MRI to EHRs and clinical documentation. It delivers advanced reasoning capabilities through specialized agentic architectures and allows for the integration of custom models and Hugging Face components. MONAI Multimodal enables developers to focus on innovation and research while addressing the unique challenges of medical data integration.
Researchers and developers can contribute to MONAI in several ways. They can integrate their models or tools directly into the MONAI framework, contribute to the MONAI Model Zoo, or collaborate via GitHub. Additionally, contributors can share pretrained models through platforms like Hugging Face or link to their repositories as featured community projects. MONAI welcomes a wide range of collaboration styles, making it easy for partners to support and extend the ecosystem in ways that align with their expertise and goals.
Engage with the MONAI community today. Check out the Working Groups.
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