MONAI

MONAI: Medical Open Network for AI

What is MONAI? MONAI is a PyTorch-based, open-source framework designed to accelerate the development and deployment of AI solutions in healthcare imaging. It aims to bridge the gap between research and clinical implementation by providing standardized, high-quality tools and workflows.

How does MONAI work? MONAI offers a comprehensive ecosystem of tools covering the entire medical AI lifecycle, including:

• MONAI Core: A domain-specific framework for training state-of-the-art medical imaging AI models. It provides medical-specific transforms, UNETR architecture, a pre-trained model zoo, and automated ML pipelines.
• MONAI Label: An intelligent image annotation tool powered by AI assistance. It supports active learning for efficient data selection, multiple viewer integrations, AI-assisted annotation, and multi-user collaboration.
• MONAI Deploy: A robust framework for deploying AI models in clinical settings, featuring clinical workflow integration, DICOM & FHIR support, containerized deployment with MAP, and inference optimization.

Key Features and Benefits

• PyTorch Native: Seamless integration and flexibility with the PyTorch ecosystem.
• Research Ready: Advanced tools for medical AI research.
• Standardized: Best practices for healthcare AI development and research.
• Community Driven: Supported by global healthcare experts.
• Innovation Focused: Cutting-edge AI architectures and methods.
• Open Source Design: Apache 2.0 licensed for maximum flexibility and collaboration.

MONAI's End-to-End Medical AI Lifecycle

MONAI ensures quality and consistency at every stage of medical AI development, from data annotation to clinical deployment:

1. Data Annotation: Using MONAI Label for AI-assisted, efficient annotation.
2. Model Training: Leveraging MONAI Core for training state-of-the-art models with medical-specific transforms.
3. Deployment: Utilizing MONAI Deploy for seamless integration into clinical workflows.

Use Cases

• AI Integration in Clinical Imaging: Mayo Clinic Florida uses MONAI to integrate AI models into radiology workflows, enhancing operational efficiency and improving patient outcomes.
• Rapid Deployment with Mercure DICOM: MONAI Application Packages (MAPs) integrate with the Mercure DICOM Orchestrator for seamless DICOM integration and flexible routing in clinical environments.
• Digital Marketplace Integration: Siemens Healthineers uses MONAI Deploy for their Digital Marketplace, providing standardized AI deployment solutions globally.

Community and Support

MONAI has a vibrant and growing community of researchers, developers, and healthcare professionals. Support resources include:

• Discussion Forums: GitHub Discussions for technical discussions and community support.
• Slack Channel: Real-time chat and collaboration.
• YouTube Channel: Tutorials, demos, and presentations.
• Tutorials Repository: Notebooks and learning materials.

How to Get Involved

You can contribute to MONAI by:

• Following the Contribution Guide
• Reporting bugs and requesting features via the Issue Tracker

Why is MONAI important? MONAI is important because it accelerates the development and deployment of AI in healthcare imaging, leading to improved diagnostics, more efficient workflows, and better patient outcomes.