Nvidia is advancing medical imaging with MONAI, its open-source research and development platform for AI applications used in medical imaging, enabling faster processing of the 3.6 billion imaging tests performed annually worldwide. According to Nvidia, speeding up the processing and evaluation of all these X-rays, CT scans, MRIs and ultrasounds is essential to helping doctors manage their workloads and improving health outcomes.
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Siemens Healthineers Integrates MONAI Deploy
At the annual meeting of RSNA, the Radiological Society of North America, Nvidia announced that Siemens Healthineers has adopted MONAI Deploy, a module within MONAI that bridges the gap from research to clinical production, to boost the speed and efficiency of integrating AI workflows for medical imaging into clinical deployments.
Siemens Healthineers' Syngo Carbon and syngo.via platforms, installed in over 15,000 devices globally, now integrate MONAI Deploy. This reduces AI deployment time from months to a few clicks, enhancing radiologists’ ability to interpret X-rays, CT scans, and MRIs efficiently.
MONAI Deploy
With a few lines of code, MONAI Deploy builds AI applications that can run anywhere. It is a tool for developing, packaging, testing, deploying and running medical AI applications in clinical production. Using it streamlines the process of developing and integrating medical imaging AI applications into clinical workflows, Nvidia explained.
"MONAI Deploy on the Siemens Healthineers platform has significantly accelerated the AI integration process, letting users port trained AI models into real-world clinical settings with just a few clicks, compared with what used to take months," Nvidia said on December 2.
"By accelerating AI model deployment, we empower healthcare institutions to harness and benefit from the latest advancements in AI-based medical imaging faster than ever," said Axel Heitland, head of digital technologies and research at Siemens Healthineers. "With MONAI Deploy, researchers can quickly tailor AI models and transition innovations from the lab to clinical practice, providing thousands of clinical researchers worldwide access to AI-driven advancements directly on their syngo.via and Syngo Carbon imaging platforms."
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New Foundation Models in MONAI v1.4
According to Nvidia, the updates in MONAI v1.4 and related Nvidia products include new foundation models for medical imaging, which can be customised in MONAI and deployed as Nvidia NIM microservices. The following models are now generally available as NIM microservices:
1. MAISI (Medical AI for Synthetic Imaging) is a latent diffusion generative AI foundation model that can simulate high-resolution, full-format 3D CT images and their anatomic segmentations.
2. VISTA-3D is a foundation model for CT image segmentation that offers accurate out-of-the-box performance covering over 120 major organ classes. It also offers effective adaptation and zero-shot capabilities to learn to segment novel structures.
Additionally, the new MONAI Multi-Modal Model (M3) framework extends multimodal LLMs with expert medical AI capabilities.
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Global Adoption of MONAI
According to Nvidia, healthcare institutions, academic medical centers, startups, and software providers around the world are adopting and advancing MONAI, including the German Cancer Research Center, Nadeem Lab from Memorial Sloan Kettering Cancer Center (MSK), University of Colorado School of Medicine, MathWorks, GSK, Flywheel, Alara Imaging, RadImageNet and Kitware.
Marking its fifth anniversary, MONAI has seen over 3.5 million downloads and is now available on the Siemens Healthineers Digital Marketplace. Cloud platforms providing access to MONAI include AWS HealthImaging, Google Cloud, Precision Imaging Network (part of Microsoft Cloud for Healthcare), and Oracle Cloud Infrastructure, Nvidia said.
