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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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放射治疗中的人工智能:当前的应用和未来的趋势

Paul Giraud1, Jean-Emmanuel Bibault2

  • 1INSERM UMR 1138, Centre de Recherche des Cordeliers, 75006 Paris, France; Department of Radiotherapy, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France.

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人工智能 (AI) 通过提高治疗精度和效率来增强放射治疗. 人工智能工具简化了工作流程,增加了实践的一致性,并为个性化的放射性瘤学策略铺平了道路.

关键词:
人工智能的人工智能是人工智能.自动细分系统 自动细分系统临床决策支持 临床决策支持辐射瘤学 辐射瘤学合成成像 合成成像

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科学领域:

  • 医学物理 医学物理
  • 辐射瘤学 辐射瘤学
  • 人工智能的人工智能

背景情况:

  • 计算机断层扫描和强度调制已经推进了放射治疗,提高了治疗精度,但也增加了工作流程的复杂性.
  • 随着这些技术进步,对众多卷的准确和统一划界变得至关重要.
  • 改进的计算能力使逆向规划和3D剂量分配产生成为可能.

研究的目的:

  • 探索人工智能在增强放射治疗工作流程中的作用.
  • 研究AI在提高实践均性和效率方面的潜力.
  • 讨论针对个性化辐射瘤学的预测工具的开发.

主要方法:

  • 在常规放射治疗实践中实施基于人工智能的工具.
  • 将工作流数据与临床和OMIC数据集成.
  • 开发用于临床决策的预测模型.

主要成果:

  • 人工智能工具正在提高效率,减少工作量,提高治疗的一致性.
  • 数据集成正在使概念验证预测工具的开发成为可能.
  • 这些工具显示了个性化瘤策略和剂量处方的潜力.

结论:

  • 人工智能为优化放射治疗工作流提供了重大机会,并增强了治疗的交付.
  • 预测工具虽然处于早期阶段,但对于朝着个性化辐射瘤学的进步至关重要.
  • 对大型多中心群体的前性验证对于人工智能驱动的预测模型的广泛采用是必要的.