---
title: "MONAI - Medical Open Network for AI"
description: "MONAI is the leading open-source framework for healthcare imaging AI, trusted by researchers and clinicians worldwide. Build, train, and deploy medical AI solutions with industry-standard tools."
canonical: https://project-monai.github.io/
audience: [researcher, engineer, clinician, newcomer]
last_updated: 2026-06-11
source: index.html
---
# Medical Open Network  
for Artificial Intelligence

The PyTorch-based framework for medical-imaging AI: research transforms, pre-trained models, and reproducible clinical deployment in one ecosystem. Open source, community-led.

[Get Started](core.html#quick-start) [View on GitHub](https://github.com/Project-MONAI)

[

MONAI Label

Active-learning annotation

](label.html)[

MONAI Core

Training & research

](core.html)[

MONAI Deploy

Clinical inference pipelines

](deploy.html)

9.5M+

pip installs

5K+

publications citing MONAI

40+

models in the Zoo

20+

challenge wins

Maintained by researchers and engineers at NVIDIA, NIH, King's College London, Mayo Clinic, MSKCC, Stanford, DKFZ, and 30+ other institutions.

Why MONAI

## Built for medical imaging

Domain-specific transforms, validated 3D architectures, and reproducible workflows. This is not a general ML framework retrofitted for healthcare.

1.  ### PyTorch native
    
    Built on PyTorch, so there is no new framework to learn. Standard `nn.Module`, `DataLoader`, AMP, and DDP all work as you expect.
    
2.  ### Domain-specific tooling
    
    DICOM and NIfTI I/O, spatial transforms for 3D medical volumes, segmentation metrics (Dice, Hausdorff, Surface Distance), and losses calibrated for class-imbalanced anatomy.
    
3.  ### Reproducible Bundles
    
    MONAI Bundles package weights, training configs, metadata, and inference code together. The same format powers the Model Zoo, so your work is portable from day one.
    
4.  ### Community-governed
    
    Maintained by NVIDIA, NIH, King's College London, Mayo Clinic, MSKCC, Stanford, DKFZ, and 30+ other institutions. Governance, working groups, and roadmap decisions are public.
    
5.  ### State-of-the-art architectures
    
    Reference implementations of MAISI (synthetic CT generation), UNETR and SwinUNETR (3D transformer backbones), VISTA-3D (universal segmentation), and Auto3DSeg (automated pipelines).
    
6.  ### Apache 2.0
    
    Open source under Apache 2.0. Freely usable in commercial products, research, and clinical pipelines, with no hidden licensing gotchas for production use.
    

Ecosystem

## Three projects, one pipeline

Annotate in **Label**, train in **Core**, deploy with **Deploy**. They share one bundle format and the same data conventions, so you can use any piece on its own or all three together.

Annotate

### MONAI Label

Server-side annotation with active learning. It suggests the next volume to label and refines predictions from your corrections. Plugs into 3D Slicer, OHIF, and MITK.

-   Active learning for efficient data selection
-   Multiple viewer integrations
-   AI-assisted annotation
-   Multi-user collaboration

[Explore Label](label.html)

Train

### MONAI Core

The training and research library. Medical-imaging transforms, 3D architectures, losses, and metrics: the building blocks researchers reach for first.

-   Medical-specific transforms
-   MAISI, UNETR & VISTA-3D architectures
-   Pre-trained model zoo
-   Automated ML pipelines

[Explore Core](core.html)

Deploy

### MONAI Deploy

The path from trained model to clinical pipeline. DICOM and FHIR I/O, containerized MAP packaging, and inference runtimes from workstation to Kubernetes.

-   Clinical workflow integration
-   DICOM & FHIR support
-   Containerized deployment
-   Inference optimization

[Explore Deploy](deploy.html)

Connect

## Community & Support

Questions, contributions, and discussions are public. Ask on GitHub or Slack; attend working-group calls; present at the MONAI Bootcamp.

### Discussion Forums

[

#### GitHub Discussions

Technical discussions & support

](https://github.com/Project-MONAI/MONAI/discussions)[

#### Slack Channel

Real-time chat & collaboration

](https://join.slack.com/t/projectmonai/shared_invite/zt-3hucgm02q-i8Bn9XofDZs2UGOH4jUl4w)

### Events & Learning

[

#### YouTube Channel

Tutorials, demos & presentations

](https://www.youtube.com/@projectmonai)[

#### Tutorials Repository

Notebooks & learning materials

](https://github.com/Project-MONAI/tutorials)

### Get Involved

[

#### Contribution Guide

Learn how to contribute

](https://github.com/Project-MONAI/MONAI/blob/master/CONTRIBUTING.md)[

#### Issue Tracker

Report bugs & request features

](https://github.com/Project-MONAI/MONAI/issues)

Impact

## Success Stories

How hospitals, vendors, and research groups run MONAI in production.

![Mayo Clinic](/assets/img/logos/mayo-clinic.png) Featured

### Clinical AI Integration at Mayo Clinic

The Center for Augmented Intelligence at Mayo Clinic Florida runs MONAI-packaged models inside its clinical Radiology pipeline. The case study documents the integration pattern, MAP containerization, and throughput results.

Clinical Integration Radiology AI Workflow

[Read Case Study](mayo-case-study.html)

 [![MONAI models running in the CAII clinical viewer at Mayo Clinic: MRI-unsafe device detection on a chest X-ray, breast-density classification on mammography, white-matter disease segmentation on brain MRI, and coronary-artery stenosis detection on CTA](/assets/img/figures/mayo-case-study-figure-3-thumb.jpg) Mayo Clinic Florida · CAII viewer](mayo-case-study.html)

![Mercure](/assets/img/logos/mercure.png)

### Mercure DICOM Orchestration

MONAI Application Packages run inside the Mercure DICOM Orchestrator, so a trained model can join a radiology routing workflow without custom glue code.

DICOM MAP Orchestration

[Read Blog](https://monai.medium.com/rapid-deployment-of-monai-application-packages-maps-in-radiology-workflows-using-the-mercure-fe7cfd77acce)

![Siemens Healthineers](/assets/img/logos/siemens.png)

### Enterprise AI at Scale

Siemens Healthineers adopted MONAI Deploy for their Digital Marketplace, enabling enterprise-scale AI deployment globally.

Enterprise Global Marketplace

[Read Blog](https://blogs.nvidia.com/blog/rsna-siemens-healthineers-monai-medical-imaging-ai/)

![Mayo Clinic](/assets/img/logos/mayo-clinic.png) × ![Siemens](/assets/img/logos/siemens.png)

### Global AI Marketplace

Mayo Clinic AI apps built with MONAI accessible to 10,000+ institutions via Siemens Digital Marketplace.

10K+ Institutions Zero-Code

[Read Case Study](mayo-siemens-case-study.html)

[View All Success Stories](successstories.html)

Contributors

## Maintained by the medical-imaging community

These institutions have dedicated engineering, clinical, and research staff to MONAI. Contributions are reviewed and merged in the open.

[Join Project MONAI](https://github.com/Project-MONAI/MONAI/blob/master/CONTRIBUTING.md)

![Answer Digital](/assets/img/logos/answer-digital.png)

![CAS](/assets/img/logos/cas.png)

![DKFZ](/assets/img/logos/dkfz.png)

![FNLCR](/assets/img/logos/fnlcr.png)

![Guy's and St Thomas'](/assets/img/logos/guys-and-st-thomas.png)

![King's College London](/assets/img/logos/kcl.png)

![Kitware](/assets/img/logos/kitware.png)

![Mayo Clinic](/assets/img/logos/mayo-clinic.png)

![MGH & BWH](/assets/img/logos/mgh-bwh.png)

![MSKCC](/assets/img/logos/mskcc.png)

![NIH NCI](/assets/img/logos/nih-nci.png)

![NVIDIA](/assets/img/logos/nvidia.png)

![Stanford](/assets/img/logos/stanford.png)

![TUM](/assets/img/logos/tum.png)

![UCL](/assets/img/logos/ucl.png)

![Vanderbilt](/assets/img/logos/vanderbilt.png)

![Warwick](/assets/img/logos/warwick.png)