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
1Department of Radiology, Emory School of Medicine, Atlanta, Georgia (J.B.).
Academic radiology
|March 4, 2026
概括
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.
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.
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