monai
AI Toolkit for Healthcare Imaging
Decision gist · record as of 2026-08-14
Yes. MONAI is actively maintained, has low install friction, carries a permissive license, and provides a mature, well-documented framework purpose-built for medical imaging deep learning. It is suitable for researchers and practitioners building healthcare imaging applications with PyTorch. No known security vulnerabilities as of the query date.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch and NumPy; GPU support is optional but recommended for medical imaging workloads.
- Low install friction with a pure-Python wheel distribution.
- Actively maintained with a recent release 53 days ago and ongoing development; the project has 8597 stars and supports current Python versions (3.10–3.13).
License · maintenance · safety
Apache License 2.0 (permissive) — Apache License 2.0 is permissive, allowing commercial and private use with minimal restrictions beyond attribution and liability disclaimers.
last release 2026-06-22 (53 days) · last repo commit 2026-08-14 · 8,597 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 542,753 downloads/mo, #6,088 on PyPI
Alternatives
Verify before relying
pip install monai
import monai
from monai.transforms import Compose, LoadImage
transforms = Compose([LoadImage(image_only=True)])- Whether the package includes pre-trained models or requires users to train from scratch.
- Performance characteristics and typical training time for common medical imaging tasks.
- Specific medical imaging modalities (CT, MRI, ultrasound, etc.) that are best supported.
What it is and what it does
MONAI is a PyTorch-based framework designed for deep learning in healthcare imaging. It sits on top of NumPy and PyTorch and provides domain-specific building blocks—transforms for preprocessing multi-dimensional medical data, network architectures, loss functions, and evaluation metrics—aimed at researchers and clinicians building end-to-end training workflows. The framework is part of the PyTorch Ecosystem and emphasizes compositional, portable APIs that integrate into existing workflows.
The package targets academic, industrial, and clinical researchers. It handles the standardized, optimized setup of deep learning models for medical imaging, reducing boilerplate and allowing users to focus on domain logic rather than infrastructure. Multi-GPU and multi-node data parallelism are built in. Installation is straightforward via pip, and the project is actively maintained with support for modern Python versions.
Use it for
- Build and train classification or segmentation models on medical imaging datasets (CT, MRI scans).
- Preprocess multi-dimensional medical imaging data with domain-specific transforms.
- Evaluate deep learning models using healthcare-specific metrics and loss functions.
- Deploy end-to-end training workflows for clinical imaging applications.
- Leverage community-contributed models from the MONAI Model Zoo for transfer learning.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
MONAI is actively maintained, has low install friction, carries a permissive license, and provides a mature, well-documented framework purpose-built for medical imaging deep learning. It is suitable for researchers and practitioners building healthcare imaging applications with PyTorch. No known security vulnerabilities as of the query date.
Install
monai on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Actively maintained with a recent release 53 days ago and ongoing development; the project has 8597 stars and supports current Python versions (3.10–3.13).
Requires PyTorch and NumPy; GPU support is optional but recommended for medical imaging workloads.
License in practice
Apache License 2.0 is permissive, allowing commercial and private use with minimal restrictions beyond attribution and liability disclaimers.
Quickstart
pip install monai
import monai
from monai.transforms import Compose, LoadImage
transforms = Compose([LoadImage(image_only=True)])Verify before relying
- Whether the package includes pre-trained models or requires users to train from scratch.
- Performance characteristics and typical training time for common medical imaging tasks.
- Specific medical imaging modalities (CT, MRI, ultrasound, etc.) that are best supported.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagestorchnumpy |
| Maintenance | Actively maintained 53 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 542,753 / month, #6,088 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchProgramming Language :: C++Programming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Information AnalysisTopic :: Scientific/Engineering :: Medical Science Apps.Topic :: Software DevelopmentTopic :: Software Development :: LibrariesTyping :: Typed |
Evidence: monai-1.6.0-202606221745-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “medical imaging deep learning”
- monaiMONAI is a PyTorch-based framework for building deep learning models…
- torchxrayvisionTorchXRayVision provides pre-trained deep learning models and unified…
- TotalSegmentatorAutomated segmentation of anatomical structures in CT and MR medical…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also torchio · nnunetv2 · torchxrayvision · fastai · mmdet · batchgeneratorsv2 · pytorch-ignite · torch · segmentation-models-pytorch · mmengine