MAIT — Medical Artificial Intelligence Toolbox

License, third-party components, and intended use

MAIT's own source code is distributed under the MIT License; see LICENSE for its terms, warranty disclaimer, and limitation of liability. The existing PERSIMUNE copyright notice is retained.

Third-party libraries, datasets, and other components retain their respective licenses and notices. MAIT's MIT License does not relicense those components or override their conditions. Some dependencies impose additional requirements or restrictions, including restrictions on commercial use or redistribution. See THIRD_PARTY_NOTICES.md and the applicable component's license. This notice does not establish that every combination or use is license-compatible.

MAIT is intended for research and methodological exploration. It has not been validated for clinical decision-making, diagnosis, or treatment. Example results and successful software execution do not establish clinical safety, effectiveness, or suitability for a particular purpose.

Users should independently assess data quality, study design, model assumptions, validation, and applicable legal and ethical requirements before relying on results. No commitment to ongoing maintenance or support is made unless separately agreed in writing.

Developer attribution identifies contributions to the software; it does not imply endorsement or approval by a current or former employer or institution.

MAIT provides explainable machine-learning workflows for tabular research data. This alpha package offers a CPU classification demonstration using Logistic Regression and Random Forest, with evaluation tables, a saved model, and explanation plots.

Install and run

python -m pip install "mait-pipeline[analysis]"
mait demo ./my-mait-demo

These commands apply after the PyPI release is published. For the trial release, install the MAIT wheel from TestPyPI separately from analysis dependencies sourced from PyPI. For a repository checkout, use python -m pip install ".[analysis]".

Choose a new output directory. The demo command runs the pipeline in a separate process and saves its input data, configured script, and results there. It does not modify your notebook's working directory.

To export the script without running it:

mait export-demo ./my-export

Or in Python:

from mait import export_demo
script = export_demo("./my-export")

Scope and validation

The installed demo completed on Apple Silicon macOS with Python 3.11, including a fresh analysis-environment installation and installation of the uploaded TestPyPI wheel. Other platforms, model combinations, survival analysis, regression, and equivalence to historical versions remain unverified. This small demonstration does not establish clinical predictive performance.

The analysis extra still includes legacy dependencies that will be separated as MAIT becomes a modular library. LightGBM is excluded; its separate lightgbm extra also needs a native OpenMP runtime on macOS.

The packaged template is synchronized with the repository tutorial template and checked automatically. See the source repository for methods, tutorials, limitations, and release instructions.

Citation

Ramtin Zargari Marandi, Anne Svane Frahm, Jens Lundgren, Daniel Dawson Murray, and Maja Milojevic. Medical artificial intelligence toolbox (MAIT): an explainable machine learning framework for binary classification, survival modelling, and regression analyses. 2025. https://doi.org/10.48550/arXiv.2501.04547