社区驱动的放射性AI在医学成像中部署的现状
Vikash Gupta1, Barbaros Erdal1, Carolina Ramirez2
1Mayo Clinic, Jacksonville, FL, United States.
JMIR AI
|December 9, 2024
概括
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.
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.
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