AI-Powered Medical Imaging

The advent of deep learning frameworks has significantly improved medical imaging AI development and deployment. However, the lack of simple, streamlined development workflows for training and building state-of-the-art deep learning models limits AI scalability to clinical production.

MONAI, an open-source medical AI framework, empowers developers and researchers to build and train multimodal algorithms and models. It provides advanced tools for training and deploying AI models into clinical production, fostering rapid innovation and reducing time-to-market. MONAI supports the creation of ground truth, model development, and management in production environments. Its curated library, Model Zoo, offers generative AI models that generate synthetic high-quality data for training deep learning models, enabling a swift start to AI development.

With MONAI open-source toolkit of foundation models, reference workflows, and interoperable building blocks, researchers and developers can:

• Accelerate development with pretrained models, standardized interfaces, and specialized components that allow for the integration of custom models.
• Develop and deploy AI models quickly and efficiently, achieving superior accuracy and results.
• Access pretrained models through Model Zoo for accelerated training and deployment, significantly reducing the time required to bring AI solutions to market.
• Build robust AI solutions optimized for clinical use.

2D and 3D visualizations of a simulated abdominal CT scan.

The rapid advancements in AI have transformed medical imaging, enabling real-time analysis, enhanced interpretation, precise segmentation, and high-performance inference. These capabilities are crucial for improving diagnostic accuracy, accelerating clinical workflows, and ultimately enhancing patient care.

NVIDIA's AI imaging analysis and inference solutions empower developers and researchers to perform real-time image analysis, enhanced image interpretation, and precise image segmentation and quantification. These solutions leverage NVIDIA's advanced tools and platforms, including GPUs and SDKs, to deliver high-performance inference capabilities that accelerate medical imaging workflows. To bridge the gap between AI development and production, NVIDIA offers pre-optimized models and industry-standard APIs to build powerful medical AI applications.

With NVIDIA's AI imaging analysis and inference solutions, researchers and developers can:

• Analyze images from different imagery modalities, including ultrasound videos, CTs, and MRIs, identifying potential disease-risk biomarkers.
• Perform real-time image analysis to quickly and accurately process medical images, improving diagnostic speed and accuracy.
• Enhance image interpretation and segmentation using advanced AI models for deeper insights and precise measurements, supporting better clinical decisions.
• Leverage high-performance inference to optimize AI models for faster and more efficient processing, reducing time-to-market for AI-driven medical imaging applications.

Segmenting 104 anatomical structures in whole-body CT scan. (Link)

Medical agents combine conversational AI technologies with medical data analysis, finding use cases in nearly every aspect of healthcare—from radiological report generation to interactive control of surgical robotic systems to medical student training.

NVIDIA MONAI has a proven track record in medical image analysis AI, from classifying lung X-ray images for COVID to winning challenges in abdominal aortic segmentation in CT images, cell labeling in pathology images, and instrument tracking in robot-assisted laparoscopic videos.

With VILA-M3, NVIDIA’s multimodal radiology agent framework, trained medical image analysis models contribute annotations or classifications to the conversational context of large vision and language models like Llama3. VILA-M3 is available as part of the open-source MONAI platform and has been used to enhance the VILA LLM as a pretrained foundational model for brain tumor MRI image interpretation. VILA-M3 sets new standards for accuracy and ease of fine-tuning among medical co-pilots.

Combining VILA-M3 with NVIDIA’s edge and cloud accelerated computing platforms such as Holoscan and NVAIE, researchers and application developers can:

• Evaluate improved accuracy by integrating medical imaging AI experts as resources for additional context in VLM conversations.
• Refine or extend VILA-M3 capabilities by refining the foundational VILA or adding new MONAI-trained medical imaging AI models as additional experts covering other modalities or diseases.
• Explore alternative LLM+expert paradigms for the continual advancement of VLMs as medical imaging co-pilots.

MONAI Multimodal AI Assistant for Radiology Workflow Analysis.