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Issues And Trends In Healthcare Delivery System01:29

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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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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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人工智能,机器学习和辐射瘤学中的大数据

Simeng Zhu1, Sung Jun Ma1, Alexander Farag2

  • 1Department of Radiation Oncology, The Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, The Ohio State University Comprehensive Cancer Center, 460 West 10th Avenue, Columbus, OH 43210, USA.

Hematology/oncology clinics of North America
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概括

人工智能和机器学习 (AI/ML) 正在彻底改变放射瘤学. 这些技术,包括计算机视觉和自然语言处理,有望提高患者护理的精度和效率.

关键词:
人工智能的人工智能是人工智能.大数据就是大数据.计算机视觉 计算机视觉 计算机视觉机器学习是机器学习.自然语言处理自然语言处理.辐射瘤学 辐射瘤学

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科学领域:

  • 在瘤学瘤学.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 放射性瘤学在数据分析和临床工作流程效率方面面临着挑战.
  • 整合先进的计算技术对于进步至关重要.

研究的目的:

  • 审查人工智能和机器学习 (AI/ML) 在辐射瘤学的应用.
  • 专注于计算机视觉 (CV) 和自然语言处理 (NLP) 技术.
  • 评估AI/ML在现场的临床实用性和潜力.

主要方法:

  • 在辐射瘤学中AI/ML应用的文献综述.
  • 基于CV的AI/ML在数字病理学和放射学方面的检查.
  • 基于NLP的AI/ML在临床文档,知识评估和质量保证中的分析.

主要成果:

  • 基于CV的AI/ML通过前性临床研究表明在数字病理学和放射学方面具有实用性.
  • 基于NLP的AI / ML展示了分析临床文档,评估知识和确保质量的应用.
  • 对于广泛的临床采用存在确定的挑战.

结论:

  • AI/ML,特别是CV和NLP,对放射性瘤学具有变革性的潜力.
  • 这些技术可以显著提高护理的精度,效率和质量.
  • 克服采用挑战是实现AI/ML在临床实践中的全部好处的关键.