MHub.ai: A Standardized Platform for Reproducible AI Research in Medical Imaging.
Authors
Abstract
Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are limited by the wide variety of AI implementations and architectures, inconsistent documentation, and reproducibility issues. Here, we introduce MHub.ai, an open-source, container-based platform that standardizes access to AI models with minimal configuration, promoting accessibility and reproducibility in medical imaging. MHub.ai packages models from peer-reviewed publications into standardized containers that support direct processing of DICOM and other formats, provide a unified application interface, and embed structured metadata. Each model is accompanied by publicly available reference data to confirm model operation. MHub.ai includes an initial set of segmentation, prediction, and feature extraction models for different modalities. The modular framework enables adaptation of any model and supports community contributions. We demonstrate the utility of the platform through comparative evaluation of lung segmentation models on public clinical data, and publicly release the generated segmentations and evaluation metrics as interactive dashboards to emphasize transparency and to enable case-level inspection. By enabling side-by-side benchmarking with identical execution commands and standardized outputs, MHub.ai reduces technical friction for model execution, evaluation, and comparison.