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人工智能 (AI) 可以提高医学成像效率,但临床翻译面临挑战. 这篇论文探讨了放射学工作流程中的AI,并介绍了人工智能医疗开放网络 (MONAI) 来弥合差距.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 放射学工作流程 放射学工作流程

背景情况:

  • 由于医疗成像数据不断增长,放射科医生的工作量正在增加.
  • 人工智能显示出提高医疗图像分析效率的潜力.
  • 人工智能研究与放射学中的临床应用之间存在差距.

研究的目的:

  • 提供AI在医学成像中的概述.
  • 强调标准在放射学工作流程中的重要性.
  • 确定在临床环境中部署人工智能的挑战.

主要方法:

  • 检查了当前的放射学工作流程和AI实施挑战.
  • 开发了人工智能使用案例的分类学,并提供了现实世界的整合例子.
  • 介绍了人工智能医疗开放网络 (MONAI) 作为解决方案.

主要成果:

  • 确定了将人工智能集成到医院放射学工作流程中的重大障碍.
  • 在医院内展示了实用的AI整合示例.
  • 提出MONAI作为可重复的深度学习解决方案的工具.

结论:

  • 应对人工智能实施挑战需要标准化的方法.
  • MONAI 提供了一个成功在放射学中整合人工智能的框架.
  • 弥合研究到临床的翻译差距对于人工智能采用至关重要.