MONAI Deploy
MONAI Deploy provides a standardized framework that simplifies deployment while ensuring reliability, performance, and seamless integration with existing healthcare infrastructure.
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Why MONAI Deploy?
Accelerate your journey from research to clinical deployment with a framework designed specifically for healthcare AI, offering unique advantages over traditional deployment approaches.
Standardized Packaging
Single MAP format for all environments
Reproducible multi-site deployments
Built-in dependency management
Healthcare-Native
DICOM and PACS integration
Clinical workflow optimization
Healthcare compliance ready
Scalable Architecture
Horizontal scaling capabilities
Real-time performance monitoring
Load balancing & failover
Enterprise Deployment Features
MONAI Deploy provides a comprehensive platform for deploying AI models in clinical environments. Discover the key features that make enterprise deployment seamless and secure.
Researcher and Developers
Trained Model
Models trained using MONAI Core or MONAI Label, or any compatible PyTorch model that can be packaged for deployment.
1. Trained
Model
MONAI Deploy App SDK
A Python SDK that provides tools and APIs to build, test, and package AI applications into standardized MONAI Application Packages (MAPs).
2. MONAI Deploy
App SDK
MONAI App Package (MAP)
A standardized container format that packages AI models, preprocessing/postprocessing logic, and dependencies into a portable, deployment-ready application.
3. MONAI App
Package (MAP)
Hospital Operations
Inference Engine
Executes AI models packaged as MAPs, providing efficient and scalable inference capabilities for medical imaging applications.
4. Inference
Engine
MONAI Deploy Workflow Manager
Orchestrates the flow of data and tasks between components, managing complex AI pipelines and ensuring reliable execution.
5. MONAI Deploy
Workflow Manager
MONAI Deploy Informatics Gateway
Handles DICOM and FHIR data exchange, providing secure and standardized communication with healthcare systems.
6. MONAI Deploy
Informatics Gateway
PACS
Picture Archiving and Communication System that stores and distributes medical images throughout the healthcare enterprise.
7. PACS
Development Pipeline
Trained Model
Models trained using MONAI Core or MONAI Label
MONAI Deploy App SDK
Build and package AI applications
MONAI App Package
Standardized deployment container
Hospital Operations
Inference Engine
Execute AI models efficiently
Workflow Manager
Orchestrate data flow and tasks
Informatics Gateway
Handle DICOM and FHIR data exchange
PACS
Connect with hospital imaging systems
Technical Components
App Development
MONAI Application Package
A Pythonic SDK to build deploy-ready AI Apps in Healthcare. Create standardized, portable applications ready for deployment.
Workflow Manager
Orchestration Hub
Central orchestration system for the MONAI Deploy Platform. Manage complex workflows and coordinate between different components seamlessly.
Informatics Gateway
Data Integration
Facilitates seamless I/O for DICOM and FHIR data. Connect AI Applications to Healthcare Information Systems with standardized protocols.
Getting Started with MONAI Deploy App SDK
Follow these steps to begin deploying your AI models using MONAI Deploy.
Environment Setup
Install MONAI Deploy SDK and set up your development environment using our comprehensive setup guide.
Application Development
Run your first MONAI Deploy application using our simple imaging example.
Package Creation
Create a MONAI Application Package (MAP) to ensure consistent deployment across different environments.
Deployment
Deploy your packaged application to your clinical environment with our deployment guide.
Real-World Applications
Discover how leading healthcare organizations are leveraging MONAI Deploy to transform patient care through AI-powered medical imaging.
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Siemens Healthineers
Enterprise IntegrationSiemens Healthineers has integrated MONAI Deploy into their AI-Rad Companion platform, enabling seamless deployment of AI models across their extensive healthcare network. This integration has significantly reduced deployment time and improved model performance monitoring.
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Mayo Clinic
Healthcare InnovationThe Center for Augmented Intelligence in Imaging at Mayo Clinic Florida has developed infrastructure for seamless integration of imaging AI models using MONAI.
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AI Centre for Value Based Healthcare
Research PlatformAIDE, the AI Deployment Engine, enables safe and efficient deployment of AI models by seamlessly integrating them into research workflows.
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mercure
Open SourceAn open-source DICOM orchestrator that seamlessly integrates MONAI Application Packages (MAPs), streamlining the deployment of AI models for research.
How to Cite MONAI Deploy
If you use MONAI Deploy in your research, please cite our paper:
@article{gupta2024monai, title={Current State of Community-Driven Radiological AI Deployment in Medical Imaging}, author={Gupta, Vikash and Erdal, Barbaros and Ramirez, Carolina and Floca, Ralf and Genereaux, Bradley and Bryson, Sidney and Bridge, Christopher and Kleesiek, Jens and Nensa, Felix and Braren, Rickmer and Younis, Khaled and Penzkofer, Tobias and Bucher, Andreas Michael and Qin, Ming Melvin and Bae, Gigon and Lee, Hyeonhoon and Cardoso, M Jorge and Ourselin, Sebastien and Kerfoot, Eric and Choudhury, Rahul and White, Richard D and Cook, Tessa and Bericat, David and Lungren, Matthew and Haukioja, Risto and Shuaib, Haris}, journal={JMIR AI}, volume={3}, pages={e55833}, year={2024}, doi={10.2196/55833} }
Get Involved in the Community
Join our growing community of researchers, developers, and healthcare professionals. Get help, share your work, and contribute to advancing medical AI deployment.
Documentation
Access comprehensive documentation covering everything from basic concepts to advanced deployment strategies.
GitHub Repository
Explore the source code, contribute to development, and stay up to date with the latest features and improvements.
Join the Discussion
Connect with the MONAI community on Slack. Get help, share ideas, and collaborate with other developers.
Sample Applications
Explore example applications and learn from real-world deployment scenarios.