In a bid to improve the efficiency of creating ground-truth data and training AI models, NVIDIA has introduced a specialized cloud service for AI in medical imaging. The recent launch of NVIDIA MONAI fog APIs at the annual meeting of the Radiological Society of North America in Chicago marks a notable advancement in integrating AI into medical scanning procedures. These APIs, a result of collaboration between NVIDIA and King’s College London within the MONAI open-source project, offer developers and service providers a more streamlined approach to incorporating AI into their medical imaging solutions by utilizing pre-trained models and advanced AI workflows.
Medical imaging plays a crucial role across various sectors, including biotech research, clinical diagnostics, and real-time decision-making in healthcare. With the wide-ranging applications of medical scanning, the need for an AI infrastructure tailored to imaging is essential. This infrastructure enables efficient data management, generation of annotations, accelerated model development, and seamless deployment of AI applications in diverse medical fields.
By utilizing the NVIDIA MONAI fog APIs, providers of solutions can empower oncologists, researchers, and clinical teams to swiftly develop specialized AI frameworks within their medical imaging platforms. Accessible through the NVIDIA DGX Cloud AI computation services, these APIs provide a pathway to enhanced AI capabilities.
Leading health imaging platform Flywheel has already integrated the NVIDIA MONAI fogAPI into its system, with further plans for adoption by medical image annotation companies such as RedBrick AI and MLOps platform providers like Dataiku.
The foundation of successful AI solutions in clinical imaging lies in a robust development framework that includes software optimizations, scalable systems, and current research methodologies. The VISTA-3D model, integrated into the NVIDIA MONAI sky APIs, enables interactive classification for clinical image analysis, supporting continuous learning and enhancing AI model efficiency over time.
Furthermore, the introduction of APIs that facilitate the creation of custom models based on MONAI pre-trained models and tools like Auto3DSeg highlights NVIDIA’s dedication to advancing AI training in medical imaging. Noteworthy accomplishments at the recent MICCAI medical imaging event demonstrated the effectiveness of these tools in addressing challenges related to analyzing 3D medical images.
Software developers and solution providers are increasingly embracing NVIDIA MONAI cloud APIs to accelerate AI innovation in medical imaging. Flywheel’s integration of MONAI via NVIDIA AI Enterprise showcases the potential for streamlining health image processing and analysis. Similarly, companies such as Redbrick AI and Dataiku are leveraging the VISTA-3D model and Auto3DSeg tool to expedite AI model development and deployment for various clinical applications.
Overall, the adoption of NVIDIA MONAI cloud APIs by industry stakeholders represents a significant stride towards democratizing AI in medical imaging, enabling quicker model development, precise classification, and enhanced clinical outcomes.
