3.6 billion. That’s about how many medical imaging tests are performed annually worldwide to diagnose, monitor and treat various conditions.
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 to improving health outcomes.
That’s why NVIDIA introduced MONAI, which serves as an open-source research and development platform for AI applications used in medical imaging and beyond. MONAI unites doctors with data scientists to unlock the power of medical data to build deep learning models and deployable applications for medical AI workflows.
This week 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.
MONAI Deploy: Accelerating AI Integration
With over 15,000 installations in medical devices around the world, the Siemens Healthineers Syngo Carbon and syngo.via enterprise imaging platforms help clinicians better read and extract insights from medical images of many sources.
Developers typically use a variety of frameworks when building AI applications. This makes it a challenge to deploy their applications into clinical environments.
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.
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. This helps researchers, entrepreneurs and startups get their applications into the hands of radiologists more quickly.
MONAI Ecosystem Boosts Innovation and Adoption
Now marking its five-year anniversary, MONAI has seen 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.
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.
MONAI Bridges Hospitals, Healthcare Startups and Research Institutions
Leading healthcare institutions, academic medical centers, startups and software providers around the world are adopting and advancing MONAI, including:
* German Cancer Research Center leads MONAI’s benchmark and metrics working group, which provides metrics for measuring AI performance and guidelines for how and when to use those metrics.
* Nadeem Lab from Memorial Sloan Kettering Cancer Center (MSK) pioneered the cloud-based deployment of multiple AI-assisted annotation pipelines and inference modules for pathology data using MONAI.
* University of Colorado School of Medicine faculty developed MONAI-based ophthalmology tools for detecting retinal diseases using a variety of imaging modalities. The university also leads some of the original federated learning developments and clinical demonstrations using MONAI.
* MathWorks has integrated MONAI Label with its Medical Imaging Toolbox, bringing medical imaging AI and AI-assisted annotation capabilities to thousands of MATLAB users engaged in medical and biomedical applications throughout academia and industry.
* GSK is exploring MONAI foundation models such as VISTA-3D and VISTA-2D for image segmentation.
* Flywheel offers a platform, which includes MONAI for streamlining imaging data management, automating research workflows, and enabling AI development and analysis, that scales for the needs of research institutions and life sciences organizations.
* Alara Imaging published its work on integrating MONAI foundation models such as VISTA-3D with LLMs such as Llama 3 at the 2024 Society for Imaging Informatics in Medicine conference.
* RadImageNet is exploring the use of MONAI’s M3 framework to develop cutting-edge vision language models that utilize expert image AI models from MONAI to generate high-quality radiological reports.
* Kitware is providing professional software development services surrounding MONAI, helping integrate MONAI into custom workflows for device manufacturers as well as regulatory-approved products.
Conclusion
MONAI has revolutionized the medical imaging industry by providing an open-source platform for AI applications. Its adoption by Siemens Healthineers and other leading healthcare institutions, academic medical centers, startups and software providers has accelerated the development and deployment of AI-powered medical imaging applications.
FAQs
Q: What is MONAI?
A: MONAI is an open-source research and development platform for AI applications used in medical imaging and beyond.
Q: What is MONAI Deploy?
A: MONAI Deploy is a module within MONAI that bridges the gap from research to clinical production, enabling the development, packaging, testing, deploying and running of medical AI applications in clinical production.
Q: What are the benefits of using MONAI?
A: MONAI provides an open-source platform for AI applications, enabling researchers and clinicians to build deep learning models and deployable applications for medical AI workflows, and streamlining the process of developing and integrating medical imaging AI applications into clinical workflows.
Q: What are some of the organizations that have adopted MONAI?
A: Siemens Healthineers, German Cancer Research Center, Memorial Sloan Kettering Cancer Center, University of Colorado School of Medicine, MathWorks, GSK, Flywheel, Alara Imaging, RadImageNet, and Kitware are some of the organizations that have adopted MONAI.
