人工智能在肌肉骨成像中的承诺和局限性
Patrick Debs1, Laura M Fayad1,2,3
1The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, MD, United States.
Frontiers in radiology
|August 23, 2023
概括
人工智能 (AI) 正在改变肌肉骨成像,帮助从调度到报告生成的任务. 虽然人工智能显示出巨大的前景,但其广泛的临床采用仍然面临挑战.
科学领域:
- 肌肉骨成像系统的成像
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
- 卷积神经网络是一种卷积神经网络.
背景情况:
- 深度学习和卷积神经网络正在推动人工智能 (AI) 的发展.
- 人工智能有可能彻底改变肌肉骨成像工作流程的各个阶段.
- 这些阶段包括订单,调度,记录,图像采集和报告生成.
研究的目的:
- 审查AI应用在肌肉骨成像中的成功和局限性.
- 探索AI如何增强放射科医生服务,提高效率,提高患者和提供者的满意度.
主要方法:
- 这是一篇综述文章,综合了现有的研究和AI在肌肉骨成像中的应用.
- 该审查审查了成像周期内的解释性和非解释性任务.
主要成果:
- 人工智能在整个肌肉骨成像周期中表现出显著的潜力,从图像获取到报告生成.
- 当前的人工智能工具面临的挑战是阻碍其无整合到临床实践中.
结论:
- 人工智能为增强肌肉骨成像服务提供了巨大的好处,从而提高了效率和满意度.
- 解决当前的局限性对于AI在放射学中的成功临床实施至关重要.
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