Kumar Yogesh, Cardan Rex A, Chang Ho-Hsin, Heinzman Katherine A, Gultekin Kadir, Goss Amy, McDonald Andrew, Murdaugh Donna, McConathy Jonathan, Rothenberg Steven, Smith Andrew D, Fiveash John, Cardenas Carlos E
Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL 35233, USA.
Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Diagnostics (Basel). 2025 Mar 21;15(7):803. doi: 10.3390/diagnostics15070803.
Medical research institutions are increasingly leveraging artificial intelligence (AI) to enhance the processing and analysis of medical imaging data. However, scaling AI-driven medical image analysis often requires specialized expertise and infrastructure that individual labs may lack. A centralized solution is to establish a core facility-a shared institutional resource-dedicated to Automated Medical Image Processing and Analysis (AMIPA). This technical note offers a practical roadmap for institutions to create an AI-based core facility for AMIPA, drawing on our experience in building such a resource. We outline the key components for replicating a successful AMIPA core facility, including high-performance computing resources, robust AI software pipelines, data management strategies, and dedicated support personnel. Emphasis is placed on workflow automation and reproducibility, ensuring researchers can efficiently and consistently process large imaging datasets. By following this roadmap, institutions can accelerate AI adoption in imaging workflows and foster a shared resource that enhances the quality and productivity of medical imaging research.
医学研究机构越来越多地利用人工智能（AI）来加强医学成像数据的处理和分析。然而，扩展人工智能驱动的医学图像分析通常需要专业知识和基础设施，而个别实验室可能并不具备。一个集中化的解决方案是建立一个核心设施——一种共享的机构资源——专门用于自动医学图像处理与分析（AMIPA）。本技术说明借鉴我们建立此类资源的经验，为各机构创建基于人工智能的AMIPA核心设施提供了一份实用路线图。我们概述了复制成功的AMIPA核心设施的关键组成部分，包括高性能计算资源、强大的人工智能软件管道、数据管理策略以及专业支持人员。重点在于工作流程自动化和可重复性，确保研究人员能够高效且一致地处理大型成像数据集。通过遵循此路线图，各机构能够加速在成像工作流程中采用人工智能，并培育一种共享资源，从而提高医学成像研究的质量和生产力。
