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Related Experiment Video

Updated: Jun 23, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

MHub.ai: A Standardized Platform for Reproducible AI Research in Medical Imaging.

Hugo Aerts1,2,3, Leonard Nürnberg1,2,3, Dennis Bontempi1,2,3

  • 1Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.

Research Square
|June 22, 2026
PubMed

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Summary
This summary is machine-generated.

MHub.ai is an open-source platform standardizing artificial intelligence (AI) models for medical imaging. It enhances accessibility and reproducibility by packaging peer-reviewed AI models into containers for easier use and comparison.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Artificial intelligence (AI) offers transformative potential in medical imaging analysis and clinical research.
  • Current research and clinical applications are hindered by diverse AI implementations, inconsistent documentation, and reproducibility challenges.

Purpose of the Study:

  • To introduce MHub.ai, an open-source, container-based platform designed to standardize access to AI models in medical imaging.
  • To enhance the accessibility and reproducibility of AI models in medical imaging research and clinical practice.

Main Methods:

  • Developed MHub.ai, a platform that packages peer-reviewed AI models into standardized containers.
  • Ensured direct processing of DICOM and other formats with a unified application interface and embedded metadata.

Related Experiment Videos

Last Updated: Jun 23, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

  • Included publicly available reference data for each model to verify operation and facilitate benchmarking.

    • Main Results:

    • MHub.ai provides an initial set of segmentation, prediction, and feature extraction models for various medical imaging modalities.
    • Demonstrated platform utility via comparative evaluation of lung segmentation models on public clinical data.
    • Publicly released segmentations and evaluation metrics as interactive dashboards for transparency and case-level inspection.

    Conclusions:

    • MHub.ai reduces technical barriers for executing, evaluating, and comparing AI models in medical imaging.
    • The platform's modular framework supports adaptation of existing models and encourages community contributions.
    • Standardized outputs and side-by-side benchmarking capabilities foster greater transparency and collaboration in AI-driven medical imaging research.