Digital Event Horizon
MONAI Deploy enables developers to build AI applications that can run anywhere with just a few lines of code, streamlining the process of developing and integrating medical imaging AI applications into clinical workflows. Siemens Healthineers has adopted MONAI Deploy to accelerate the AI integration process, allowing users to port trained AI models into real-world clinical settings with ease.
NVIDIA introduces MONAI, an open-source AI platform for medical imaging, to cater to growing demand. MONAI has been adopted by various institutions worldwide, including Siemens Healthineers, for its efficiency and innovation in medical imaging. The platform has seen significant growth with over 3.5 million downloads, 220 contributors, and use in numerous clinical products. MONAI introduces a framework called MONAI Multi-Modal Model (M3) for extending multimodal LLMs with medical AI experts. The platform has partnered with cloud service providers to enable scalable AI applications. MONAI Deploy bridges the gap from research to clinical production, streamlining AI application development and integration. The adoption of MONAI by Siemens Healthineers accelerates AI integration process, allowing for faster deployment of trained AI models.
The world of medical imaging is undergoing a revolution, thanks to the advancements in Artificial Intelligence (AI) technology. The ability to process and analyze vast amounts of medical data has become crucial in diagnosing and treating various conditions. To cater to this growing need, NVIDIA has introduced MONAI, an open-source research and development platform for AI applications used in medical imaging and beyond. In a recent announcement, it was revealed that Siemens Healthineers has adopted MONAI Deploy, a module within MONAI, to boost the speed and efficiency of integrating AI workflows for medical imaging into clinical deployments.
MONAI, which stands for 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. This technology has been extensively adopted by various institutions worldwide, including German Cancer Research Center, Nadeem Lab from Memorial Sloan Kettering Cancer Center (MSK), University of Colorado School of Medicine faculty, MathWorks, GSK, Flywheel, Alara Imaging, and RadImageNet.
In addition to these adoption rates, MONAI has seen significant growth over the past five years. Since its inception, it has witnessed over 3.5 million downloads, 220 contributors from around the world, acknowledgements in over 3,000 publications, 17 MICCAI challenge wins, and use in numerous clinical products.
To facilitate the development of AI applications, MONAI introduced a framework called MONAI Multi-Modal Model (M3), which extends any multimodal LLM with medical AI experts such as trained AI models from MONAI’s Model Zoo. The power of this new framework is demonstrated by the VILA-M3 foundation model that’s now available on Hugging Face, offering state-of-the-art radiological image copilot performance.
Furthermore, MONAI has partnered with various cloud service providers to enable researchers and companies to deploy scalable AI applications. These platforms include AWS HealthImaging, Google Cloud, Precision Imaging Network (part of Microsoft Cloud for Healthcare), and Oracle Cloud Infrastructure.
MONAI Deploy bridges the gap from research to clinical production by enabling developers to build AI applications that can run anywhere with just a few lines of code. This tool streamlines the process of developing and integrating medical imaging AI applications into clinical workflows. With MONAI Deploy, researchers can tailor AI models and transition innovations from the lab to clinical practice faster than ever.
The adoption of MONAI by Siemens Healthineers has significantly accelerated the AI integration process, allowing users to port trained AI models into real-world clinical settings with just a few clicks compared to what used to take months. This empowers healthcare institutions to harness and benefit from the latest advancements in AI-based medical imaging faster than ever.
Axel Heitland, head of digital technologies and research at Siemens Healthineers, stated, “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. 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.”
The adoption of MONAI by various institutions has not only enhanced the efficiency of medical imaging but also led to significant innovation in the field. The latest release of MONAI — v1.4 — includes updates that give researchers and clinicians even more opportunities to take advantage of the innovations of MONAI and contribute to Siemens Healthineers Syngo Carbon, syngo.via, and the Siemens Healthineers Digital Marketplace.
The updates in MONAI v1.4 and related NVIDIA products include new foundation models for medical imaging, which can be customized in MONAI and deployed as NVIDIA NIM microservices. These foundation models are now generally available as NIM microservices, providing researchers and clinicians with access to cutting-edge AI technology.
The success of MONAI is a testament to the power of open-source innovation and collaboration between researchers, institutions, and industry partners. As the world continues to evolve at an unprecedented pace, technologies like MONAI will play a pivotal role in revolutionizing healthcare and improving health outcomes for millions of people worldwide.
Related Information:
https://blogs.nvidia.com/blog/rsna-siemens-healthineers-monai-medical-imaging-ai/
Published: Mon Dec 2 10:49:16 2024 by llama3.2 3B Q4_K_M
