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 that can be used to confirm model operation. MHub$.$ai includes an initial set of state-of-the-art 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 in a clinical use case through comparative evaluation of lung segmentation models. To further strengthen transparency and reproducibility, we publicly release the generated segmentations and evaluation metrics and provide interactive dashboards that allow readers to inspect individual cases and reproduce or extend our analysis. By simplifying model use, MHub$.$ai enables side-by-side benchmarking with identical execution commands and standardized outputs, and lowers the barrier to clinical translation.

翻译：人工智能（AI）有望通过自动化图像分析和加速临床研究来变革医学影像领域。然而，当前AI实现方式和架构的多样性、文档记录的不一致性以及可复现性问题限制了其研究和临床应用。本文介绍MHub.ai——一个基于容器的开源平台，该平台通过最小化配置实现对AI模型的标准化访问，从而提升医学影像领域的可及性与可复现性。MHub.ai将同行评审出版物中的模型封装至标准化容器中，这些容器支持直接处理DICOM等格式数据，提供统一的应用接口，并嵌入结构化元数据。每个模型均附带可公开获取的参考数据，用于验证模型运行状态。平台初始收录了涵盖不同模态的先进分割、预测和特征提取模型。其模块化框架支持适配任意模型，并鼓励社区贡献。我们通过对肺部分割模型的对比评估，展示了该平台在临床场景中的实用性。为增强透明度和可复现性，我们公开了生成的分割结果与评估指标，并提供交互式仪表板供读者查验具体案例、复现或扩展我们的分析。MHub.ai通过简化模型使用流程，支持使用相同执行命令和标准化输出进行并行基准测试，从而降低了临床转化的门槛。