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MONAI Deploy
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MONAI Deploy provides a standardized framework that simplifies deployment while ensuring reliability, performance, and seamless integration with existing healthcare infrastructure.
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Get Started
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Documentation
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Get Involved
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<!-- Why MONAI Deploy Section -->
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<div class="container px-4 sm:px-6 lg:px-8">
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<h2 class="text-4xl font-bold text-gray-800 mb-8 relative inline-block pb-2">
Why MONAI Deploy?
<span class="absolute bottom-0 left-0 w-full h-0.5 bg-brand-primary"></span>
</h2>
<p class="text-xl text-gray-600">
Accelerate your journey from research to clinical deployment with a framework designed specifically for healthcare AI, offering unique advantages over traditional deployment approaches.
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<!-- Standardization Card -->
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<h3 class="text-xl font-bold text-gray-800 mb-4">Standardized Packaging</h3>
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<p class="text-gray-600">Single MAP format for all environments</p>
</div>
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<p class="text-gray-600">Reproducible multi-site deployments</p>
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<p class="text-gray-600">Built-in dependency management</p>
</div>
</div>
</div>
<!-- Healthcare Integration Card -->
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<h3 class="text-xl font-bold text-gray-800 mb-4">Healthcare-Native</h3>
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<p class="text-gray-600">DICOM and PACS integration</p>
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<p class="text-gray-600">Clinical workflow optimization</p>
</div>
<div class="flex items-start gap-3">
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<p class="text-gray-600">Healthcare compliance ready</p>
</div>
</div>
</div>
<!-- Enterprise Scale Card -->
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<h3 class="text-xl font-bold text-gray-800 mb-4">Scalable Architecture</h3>
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<p class="text-gray-600">Horizontal scaling capabilities</p>
</div>
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<p class="text-gray-600">Real-time performance monitoring</p>
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<p class="text-gray-600">Load balancing & failover</p>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Features Section -->
<section class="py-24 bg-white">
<div class="container px-4 sm:px-6 lg:px-8">
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<h2 class="text-4xl font-bold text-gray-800 mb-8 relative inline-block pb-2">
Enterprise Deployment Features
<span class="absolute bottom-0 left-0 w-full h-0.5 bg-brand-primary"></span>
</h2>
<p class="text-xl text-gray-600">
MONAI Deploy provides a comprehensive platform for deploying AI models in clinical environments. Discover the key features that make enterprise deployment seamless and secure.
</p>
</div>
<div class="max-w-6xl mx-auto mb-16">
<!-- Desktop View - Interactive Diagram -->
<div class="hidden lg:block">
<!-- Researcher and Developers Section -->
<div class="mb-12 opacity-0 animate-fade-in animation-delay-300">
<h3 class="text-2xl font-bold text-gray-800 mb-8 text-center">Researcher and Developers</h3>
<div class="bg-pattern p-8 rounded-2xl">
<div class="flex items-center justify-center gap-12">
<!-- 1. Trained Model -->
<div class="flex flex-col items-center group" style="position: relative; z-index: 100;">
<div class="relative">
<div class="w-20 h-20 bg-brand-primary/10 rounded-lg shadow-lg hover:shadow-xl flex items-center justify-center p-4 transition-all duration-300 cursor-pointer transform hover:scale-110 hover:rotate-3 hover-glow" onclick="toggleTooltip('trained-model')">
<svg class="w-12 h-12 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
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<div class="hidden group-hover:block absolute left-1/2 transform -translate-x-1/2 bottom-full mb-4 w-64 bg-white p-4 rounded-lg shadow-lg border border-gray-200 tooltip-animation">
<h4 class="font-bold text-gray-800 mb-2">Trained Model</h4>
<p class="text-sm text-gray-600">Models trained using MONAI Core or MONAI Label, or any compatible PyTorch model that can be packaged for deployment.</p>
<div class="absolute bottom-0 left-1/2 transform -translate-x-1/2 translate-y-1/2 rotate-45 w-4 h-4 bg-white border-r border-b border-gray-200"></div>
</div>
</div>
<p class="mt-3 font-medium text-gray-700">
<span class="text-lg text-brand-primary font-bold">1.</span> Trained<br>Model
</p>
</div>
<!-- Single Direction Arrow -->
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<!-- 2. MONAI Deploy App SDK -->
<div class="flex flex-col items-center group" style="position: relative; z-index: 100;">
<div class="relative">
<div class="w-20 h-20 bg-sky-400/10 rounded-lg shadow-lg hover:shadow-xl flex items-center justify-center p-4 transition-all duration-300 cursor-pointer transform hover:scale-110 hover:rotate-3 hover-glow" onclick="toggleTooltip('app-sdk')">
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<div class="hidden group-hover:block absolute left-1/2 transform -translate-x-1/2 bottom-full mb-4 w-64 bg-white p-4 rounded-lg shadow-lg border border-gray-200 tooltip-animation">
<h4 class="font-bold text-gray-800 mb-2">MONAI Deploy App SDK</h4>
<p class="text-sm text-gray-600">A Python SDK that provides tools and APIs to build, test, and package AI applications into standardized MONAI Application Packages (MAPs).</p>
<div class="absolute bottom-0 left-1/2 transform -translate-x-1/2 translate-y-1/2 rotate-45 w-4 h-4 bg-white border-r border-b border-gray-200"></div>
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<p class="mt-3 font-medium text-gray-700">
<span class="text-lg text-sky-400 font-bold">2.</span> MONAI Deploy<br>App SDK
</p>
</div>
<!-- Single Direction Arrow -->
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<!-- 3. MONAI App Package -->
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<div class="w-20 h-20 bg-brand-primary/10 rounded-lg shadow-lg hover:shadow-xl flex items-center justify-center p-4 transition-all duration-300 cursor-pointer transform hover:scale-110 hover:rotate-3 hover-glow" onclick="toggleTooltip('map')">
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<div class="hidden group-hover:block absolute left-1/2 transform -translate-x-1/2 bottom-full mb-4 w-64 bg-white p-4 rounded-lg shadow-lg border border-gray-200 tooltip-animation">
<h4 class="font-bold text-gray-800 mb-2">MONAI App Package (MAP)</h4>
<p class="text-sm text-gray-600">A standardized container format that packages AI models, preprocessing/postprocessing logic, and dependencies into a portable, deployment-ready application.</p>
<div class="absolute bottom-0 left-1/2 transform -translate-x-1/2 translate-y-1/2 rotate-45 w-4 h-4 bg-white border-r border-b border-gray-200"></div>
</div>
</div>
<p class="mt-3 font-medium text-gray-700">
<span class="text-lg text-brand-primary font-bold">3.</span> MONAI App<br>Package (MAP)
</p>
</div>
</div>
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<!-- Dashed Line Separator -->
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<!-- Hospital Operations Section -->
<div class="opacity-0 animate-fade-in animation-delay-700" style="position: relative; z-index: 1;">
<h3 class="text-2xl font-bold text-gray-800 mb-8 text-center">Hospital Operations</h3>
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<!-- 4. Inference Engine -->
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<h4 class="font-bold text-gray-800 mb-2">Inference Engine</h4>
<p class="text-sm text-gray-600">Executes AI models packaged as MAPs, providing efficient and scalable inference capabilities for medical imaging applications.</p>
<div class="absolute bottom-0 left-1/2 transform -translate-x-1/2 translate-y-1/2 rotate-45 w-4 h-4 bg-white border-r border-b border-gray-200"></div>
</div>
<p class="mt-3 font-medium text-gray-700">
<span class="text-lg text-red-500 font-bold">4.</span> Inference<br>Engine
</p>
</div>
<!-- Bidirectional Arrow -->
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<!-- 5. Workflow Manager -->
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<h4 class="font-bold text-gray-800 mb-2">MONAI Deploy Workflow Manager</h4>
<p class="text-sm text-gray-600">Orchestrates the flow of data and tasks between components, managing complex AI pipelines and ensuring reliable execution.</p>
<div class="absolute bottom-0 left-1/2 transform -translate-x-1/2 translate-y-1/2 rotate-45 w-4 h-4 bg-white border-r border-b border-gray-200"></div>
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<p class="text-sm text-gray-600">Handles DICOM and FHIR data exchange, providing secure and standardized communication with healthcare systems.</p>
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<span class="text-sm font-medium text-gray-700">MONAI Artifacts</span>
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<span class="text-sm font-medium text-gray-700">MONAI Subsystems</span>
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<h4 class="font-bold text-gray-800">Trained Model</h4>
<p class="text-sm text-gray-600">Models trained using MONAI Core or MONAI Label</p>
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<h4 class="font-bold text-gray-800">MONAI Deploy App SDK</h4>
<p class="text-sm text-gray-600">Build and package AI applications</p>
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<h4 class="font-bold text-gray-800">MONAI App Package</h4>
<p class="text-sm text-gray-600">Standardized deployment container</p>
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<p class="text-sm text-gray-600">Execute AI models efficiently</p>
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<h4 class="font-bold text-gray-800">Workflow Manager</h4>
<p class="text-sm text-gray-600">Orchestrate data flow and tasks</p>
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<h4 class="font-bold text-gray-800">Informatics Gateway</h4>
<p class="text-sm text-gray-600">Handle DICOM and FHIR data exchange</p>
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<span class="text-red-500 font-bold">7</span>
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<h4 class="font-bold text-gray-800">PACS</h4>
<p class="text-sm text-gray-600">Connect with hospital imaging systems</p>
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Technical Components
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<h4 class="text-lg font-semibold text-brand-primary mb-4">MONAI Application Package</h4>
<p class="text-gray-600">A Pythonic SDK to build deploy-ready AI Apps in Healthcare. Create standardized, portable applications ready for deployment.</p>
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<h3 class="text-xl font-bold text-gray-800 mb-2">Workflow Manager</h3>
<h4 class="text-lg font-semibold text-brand-primary mb-4">Orchestration Hub</h4>
<p class="text-gray-600">Central orchestration system for the MONAI Deploy Platform. Manage complex workflows and coordinate between different components seamlessly.</p>
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<a href="https://github.com/Project-MONAI/monai-deploy-workflow-manager" target="_blank" class="px-6 py-2.5 rounded-lg bg-white text-brand-primary border-2 border-brand-primary hover:bg-brand-light transition-colors inline-flex items-center gap-2 group">
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<h4 class="text-lg font-semibold text-brand-primary mb-4">Data Integration</h4>
<p class="text-gray-600">Facilitates seamless I/O for DICOM and FHIR data. Connect AI Applications to Healthcare Information Systems with standardized protocols.</p>
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Getting Started with MONAI Deploy App SDK
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Follow these steps to begin deploying your AI models using MONAI Deploy.
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<!-- Step 1: Environment Setup -->
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<div class="flex-shrink-0 text-3xl font-bold text-brand-primary">1</div>
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<h3 class="text-xl font-bold text-gray-800 mb-2">Environment Setup</h3>
<p class="text-gray-600 mb-6">Install MONAI Deploy SDK and set up your development environment using our comprehensive setup guide.</p>
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<span class="block">pip install monai-deploy-app-sdk</span>
<span class="block"> </span>
<span class="block"># Clone the MONAI Deploy App SDK repository for example applications</span>
<span class="block">git clone https://github.com/Project-MONAI/monai-deploy-app-sdk.git</span>
<span class="block">cd monai-deploy-app-sdk</span>
<span class="block"> </span>
<span class="block"># Install additional dependencies required for the simple imaging application</span>
<span class="block">pip install matplotlib Pillow scikit-image</span>
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<div class="flex-shrink-0 text-3xl font-bold text-brand-primary">2</div>
<div class="flex-grow">
<h3 class="text-xl font-bold text-gray-800 mb-2">Application Development</h3>
<p class="text-gray-600 mb-6">Run your first MONAI Deploy application using our simple imaging example.</p>
</div>
</div>
<div class="mt-4 relative w-full mb-6">
<div class="bg-gray-50 rounded-lg p-4 font-mono text-sm text-gray-700 w-full border border-gray-200 shadow-sm">
<span class="block"># Run the MONAI Deploy application locally</span>
<span class="block">python examples/apps/simple_imaging_app/app.py \</span>
<span class="block pl-4">-i examples/apps/simple_imaging_app/brain_mr_input.jpg \</span>
<span class="block pl-4">-o output</span>
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<div class="flex-shrink-0 text-3xl font-bold text-brand-primary">3</div>
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<h3 class="text-xl font-bold text-gray-800 mb-2">Package Creation</h3>
<p class="text-gray-600 mb-6">Create a MONAI Application Package (MAP) to ensure consistent deployment across different environments.</p>
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<div class="mt-4 relative w-full mb-6">
<div class="bg-gray-50 rounded-lg p-4 font-mono text-sm text-gray-700 w-full border border-gray-200 shadow-sm">
<span class="block"># Package the application to create a MAP Docker image</span>
<span class="block">monai-deploy package examples/apps/simple_imaging_app \</span>
<span class="block pl-4">-c simple_imaging_app/app.yaml \</span>
<span class="block pl-4">-t simple_app:latest \</span>
<span class="block pl-4">--platform x64-workstation \</span>
<span class="block pl-4">-l DEBUG</span>
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<pre class="bg-gray-50 rounded-lg p-4 font-mono text-sm text-gray-700 whitespace-pre-wrap">@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}
}</pre>
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