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关于人工智能和机器学习在放射学中的应用的RRA视角:从实验到临床可行的解决方案

Joshua Brown1, Brittany Z Dashevsky2, Dogan Polat3

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人工智能 (AI) 和机器学习增强了放射学诊断,工作流程和报告. 通过放射科医生-人工智能合作,将人工智能整合为增强人类专业知识的工具来实现最佳结果.

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科学领域:

  • 放射学 放射学是指放射学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 新兴技术正在改变放射学.
  • 人工智能 (AI) 和机器学习 (ML) 显示出巨大的潜力.
  • 这是关于放射学新兴技术的七部分系列中的第一篇评论.

研究的目的:

  • 检查AI和ML在诊断解释,工作流程优化和报告生成中的应用.
  • 审查目前在放射学AI的现状和挑战.
  • 要突出人工智能的过渡从实验到临床可行性.

主要方法:

  • 审查深度学习,多式联网大型语言模型和自然语言处理方面的进展.
  • 分析AI对准确性,效率和报告质量的影响.
  • 评估诸如绩效可变性,概括性和工作流集成等挑战.

主要成果:

  • 人工智能和机器学习提高了放射学中的准确性,效率和报告质量.
  • 关键的挑战包括可变的性能,有限的概括性和整合障碍.
  • 放射科医生-人工智能合作产生了最强大的临床结果.

结论:

  • 人工智能正在转变为一种临床上可行的技术,以增强放射学实践.
  • 经过深思熟虑的实施和适当的监督对于成功的AI集成至关重要.
  • 人工智能在增强,而不是取代放射学方面的人类专业知识时最有效.