diff --git a/README.md b/README.md
index 92e4ca7eba..bddd6a4769 100644
--- a/README.md
+++ b/README.md
@@ -1,104 +1,95 @@
-
+
-**M**edical **O**pen **N**etwork for **AI**
+**MONAI: Medical Open Network for AI**
-
[](https://opensource.org/licenses/Apache-2.0)
-[](https://arxiv.org/abs/2211.02701)
[](https://badge.fury.io/py/monai)
[](https://hub.docker.com/r/projectmonai/monai)
[](https://anaconda.org/conda-forge/monai)
-
-[](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml)
-[](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev)
[](https://monai.readthedocs.io/en/latest/)
-[](https://codecov.io/gh/Project-MONAI/MONAI)
-[](https://piptrends.com/package/monai)
-MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/dev/LICENSE) framework for deep learning in healthcare imaging, part of the [PyTorch Ecosystem](https://pytorch.org/ecosystem/).
-Its ambitions are as follows:
+MONAI is a free and open-source deep learning framework for medical imaging, built on top of [PyTorch](https://pytorch.org/) and listed in the [PyTorch Ecosystem](https://pytorch.org/ecosystem/). The project aims to:
-- Developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
-- Creating state-of-the-art, end-to-end training workflows for healthcare imaging;
-- Providing researchers with the optimized and standardized way to create and evaluate deep learning models.
+- Bring academic, industry and clinical researchers together around one shared foundation;
+- Offer modern, complete training pipelines for medical imaging tasks;
+- Give researchers a standard, well-optimized way to build and assess deep learning models.
-## Features
+## Key Capabilities
-> _Please see [the technical highlights](https://monai.readthedocs.io/en/latest/highlights.html) and [What's New](https://monai.readthedocs.io/en/latest/whatsnew.html) of the milestone releases._
+> For details, see the [technical highlights](https://monai.readthedocs.io/en/latest/highlights.html) and the [release notes](https://monai.readthedocs.io/en/latest/whatsnew.html).
-- flexible pre-processing for multi-dimensional medical imaging data;
-- compositional & portable APIs for ease of integration in existing workflows;
-- domain-specific implementations for networks, losses, evaluation metrics and more;
-- customizable design for varying user expertise;
-- multi-GPU multi-node data parallelism support.
+- Pre-processing that can be adapted to medical images of any dimensionality;
+- Modular, portable APIs that slot easily into existing code;
+- Networks, loss functions, metrics and other components designed for the medical domain;
+- A design that suits users with different levels of experience;
+- Data-parallel training across several GPUs and several machines.
-## Requirements
+## Dependencies
-MONAI works with the [currently supported versions of Python](https://devguide.python.org/versions), and depends directly on NumPy and PyTorch with many optional dependencies.
+MONAI runs on the Python versions that are still officially supported. It depends directly on NumPy and PyTorch, plus a number of optional packages.
-* Major releases of MONAI will have dependency versions stated for them. The current state of the `dev` branch in this repository is the unreleased development version of MONAI which typically will support current versions of dependencies and include updates and bug fixes to do so.
-* PyTorch support covers [the current version](https://github.com/pytorch/pytorch/releases) plus three previous minor versions. If compatibility issues with a PyTorch version and other dependencies arise, support for a version may be delayed until a major release.
-* Our support policy for other dependencies adheres for the most part to [SPEC0](https://scientific-python.org/specs/spec-0000), where dependency versions are supported where possible for up to two years. Discovered vulnerabilities or defects may require certain versions to be explicitly not supported.
-* See the `pyproject.toml` file for dependency version information.
+* Each major release lists its own dependency versions. The `dev` branch is the in-progress version and usually tracks current dependencies, including the fixes needed to keep up with them.
+* Supported PyTorch versions are the newest release and the three minor versions before it. If a PyTorch version clashes with another dependency, support for it may wait until the next major release.
+* For other dependencies the project mostly follows [SPEC0](https://scientific-python.org/specs/spec-0000), supporting versions for up to about two years. A version with a known vulnerability or defect may be explicitly dropped.
+* Exact version requirements are in `pyproject.toml`.
-## Installation
+## How to Install
-To install [the current release](https://pypi.org/project/monai/), you can simply run:
+The latest [stable release](https://pypi.org/project/monai/) installs with a single command:
```bash
pip install monai
```
-Please refer to [the installation guide](https://monai.readthedocs.io/en/latest/installation.html) for other installation options.
-
-## Getting Started
+Other ways to install are covered in the [installation guide](https://monai.readthedocs.io/en/latest/installation.html).
-[MedNIST demo](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/2d_classification/mednist_tutorial.ipynb) and [MONAI for PyTorch Users](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/modules/developer_guide.ipynb) are available on Colab.
+## Where to Begin
-Examples and notebook tutorials are located at [Project-MONAI/tutorials](https://github.com/Project-MONAI/tutorials).
+Two interactive notebooks run directly in Colab: the [MedNIST demo](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/2d_classification/mednist_tutorial.ipynb) and [MONAI for PyTorch Users](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/modules/developer_guide.ipynb).
-Technical documentation is available at [monai.readthedocs.io](https://monai.readthedocs.io).
+More examples and notebooks live in the [tutorials repository](https://github.com/Project-MONAI/tutorials). Full technical documentation is at [monai.readthedocs.io](https://monai.readthedocs.io).
## Docker
-The MONAI Docker image is available from [Dockerhub](https://hub.docker.com/r/projectmonai/monai),
-tagged as `latest` for the latest state of `dev` or with a release version. A slimmed down image can also be built
-locally using `Dockerfile.slim`, see that file for instructions.
+A ready-made image is published on [Docker Hub](https://hub.docker.com/r/projectmonai/monai). The `latest` tag follows the current `dev` branch, and release versions have their own tags. If you want a smaller image, you can build one yourself from `Dockerfile.slim`; the instructions are inside that file.
+
+To start a container with the newest MONAI and GPU access:
-To get started with the latest MONAI, use `docker run -ti --rm --gpus all projectmonai/monai:latest /bin/bash`.
+```bash
+docker run -ti --rm --gpus all projectmonai/monai:latest /bin/bash
+```
-## Citation
+## Citing MONAI
-If you have used MONAI in your research, please cite us! The citation can be exported from: .
+If MONAI helped with your research, please give credit by citing it. A citation can be exported from .
## Model Zoo
-[The MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo) is a place for researchers and data scientists to share the latest and great models from the community.
-Utilizing [the MONAI Bundle format](https://monai.readthedocs.io/en/latest/bundle_intro.html) makes it easy to [get started](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo) building workflows with MONAI.
+The [MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo) is a shared collection where the community publishes strong models. Models use the [MONAI Bundle format](https://monai.readthedocs.io/en/latest/bundle_intro.html), which makes it simple to [start building workflows](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo).
## Contributing
-For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md).
+To learn how to contribute, read the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md).
## Community
-Join the conversation on Twitter/X [@ProjectMONAI](https://twitter.com/ProjectMONAI), [LinkedIn](https://www.linkedin.com/company/projectmonai), or join our [Slack channel](https://forms.gle/QTxJq3hFictp31UM9).
+Follow or talk to the project on Twitter/X ([@ProjectMONAI](https://twitter.com/ProjectMONAI)) or [LinkedIn](https://www.linkedin.com/company/projectmonai), or join the [Slack channel](https://forms.gle/QTxJq3hFictp31UM9).
-Ask and answer questions over on [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions).
+Questions and answers are welcome in the [GitHub Discussions](https://github.com/Project-MONAI/MONAI/discussions) section.
-## Links
+## Useful Links
- Website:
-- API documentation (milestone):
-- API documentation (latest dev):
-- Code:
-- Project tracker:
-- Issue tracker:
+- Documentation (stable):
+- Documentation (latest dev):
+- Source code:
+- Project board:
+- Issues:
- Wiki:
-- Test status:
-- PyPI package:
+- CI status:
+- PyPI:
- conda-forge:
-- Weekly previews:
+- Weekly preview builds:
- Docker Hub: