Your model,
packaged for the clinic
Why MONAI Deploy
Deployment built for healthcare, not retrofitted to it
Generic MLOps stacks stop at the model server. MONAI Deploy speaks DICOM and FHIR, packages reproducibly, and scales from a research workstation to a hospital fleet.
Standardized Packaging
One MAP container format for every target environment
Reproducible multi-site deployments: same artifact, same behavior
Dependencies pinned and bundled at package time
Healthcare-Native I/O
DICOM in, DICOM/FHIR out, with PACS integration via the Informatics Gateway
Built-in operators for series selection, segmentation writing, and reports
Designed for clinical-workflow and compliance requirements
Scalable Architecture
Horizontal scaling on Kubernetes or single-node workstations
Workflow Manager orchestrates pipelines and monitors execution
Load balancing and failover for production reliability
Platform
From trained model to PACS
Researchers & developers build
Trained Model
From MONAI Core, MONAI Label, or any PyTorch checkpoint you can wrap in an operator.
App SDK
A Pythonic SDK to compose, test, and package inference apps from reusable operators.
MAP Container
Model, pre/post-processing, and dependencies in one portable, deployment-ready image.
Hospital operations run
Inference Engine
Executes MAPs efficiently: local runner, Holoscan, or cluster runtimes.
Workflow Manager
Routes studies to the right MAP and orchestrates multi-step pipelines.
Informatics Gateway
Secure DICOM and FHIR exchange with hospital information systems.
PACS
Results land back in the archive radiologists already use (third-party system).
The three subsystems, in depth
App SDK
Build & package
Compose apps from operators like DICOMSeriesToVolumeOperator and MonaiSegInferenceOperator, then package them into MAPs with the monai-deploy CLI.
Workflow Manager
Orchestrate
The central orchestration service: routes incoming studies, triggers the right MAPs, tracks task state, and coordinates clinical pipelines end to end.
Informatics Gateway
Connect
Standards-based DICOM and FHIR I/O between AI applications and hospital systems: the bridge to PACS, RIS, and EHR.
Quick Start
Install, run, package, deploy
Four steps take the bundled simple-imaging example from source to a runnable MAP container on your workstation.
Set up the environment
Install the App SDK and clone the repository for the example apps.
$ pip install monai-deploy-app-sdk
$ git clone https://github.com/Project-MONAI/monai-deploy-app-sdk.git
$ cd monai-deploy-app-sdk
$ pip install matplotlib Pillow scikit-imageRun the app locally
Execute the simple imaging example straight from Python, no container yet.
$ python examples/apps/simple_imaging_app/app.py \
-i examples/apps/simple_imaging_app/brain_mr_input.jpg \
-o outputPackage it as a MAP
Build the Docker image that runs identically in any environment.
$ monai-deploy package examples/apps/simple_imaging_app \
-c simple_imaging_app/app.yaml \
-t simple_app:latest \
--platform x64-workstationRun the packaged container
Same inputs, same outputs, now from the deployable artifact.
$ mkdir -p input && cp examples/apps/simple_imaging_app/brain_mr_input.jpg input/
$ monai-deploy run simple_app-x64-workstation-dgpu-linux-amd64:latest \
-i input -o outputCase Studies
Running in production today
The same MAP format scales from a single research PACS to a marketplace reaching 10,000+ institutions.
Siemens Healthineers
Enterprise AI Integration
MONAI Deploy is integrated into the AI-Rad Companion platform, cutting model deployment time across Siemens' global healthcare network and improving performance monitoring.
Mayo Clinic
Clinical AI Integration
The Center for Augmented Intelligence in Imaging at Mayo Clinic Florida runs MONAI-packaged models inside its clinical Radiology pipeline. The case study documents the pattern.
AI Centre for Value Based Healthcare
Research Platform
AIDE, the AI Deployment Engine, safely deploys AI models into research workflows across the UK's AI Centre hospital network.
Mercure
DICOM Orchestration Platform
An open-source DICOM orchestrator with native MAP support: drop a MONAI Application Package into a radiology research workflow without custom glue code.
× Mayo Clinic & Siemens Digital Marketplace
Scalable Research Deployment
Mayo Clinic AI applications built with MONAI are available to over 10,000 institutions through the Siemens Digital Marketplace: zero-code, globally scaled deployment of MONAI-powered solutions.
Reference
How to cite MONAI Deploy
If MONAI Deploy supports your research, please cite the JMIR AI 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}
} Connect
Deploy with the community
The Deploy Working Group meets in the open. Bring your integration questions, hospital constraints, and deployment war stories.
Documentation
Concepts, tutorials, and deployment strategies for the full Deploy stack.
Deploy Working Group
The group steering the MAP spec, subsystems, and clinical-integration patterns.