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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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病理学中的人工智能:当前的应用,局限性和未来的方向.

Akhil Sajithkumar1, Jubin Thomas2, Ajish Meprathumalil Saji2

  • 1Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India. aksmvpa@gmail.com.

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人工智能 (AI) 准备彻底改变数字病理学,增强基于图像的诊断和提高效率. 这篇论文探讨了AI.

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科学领域:

  • 数字病理学和人工智能的人工智能
  • 医学图像分析 医学图像分析
  • 深度学习在医疗保健中的应用

背景情况:

  • 人工智能 (AI) 最近的进步,特别是计算机视觉,激起了人们对其在病理学中的应用产生重大兴趣.
  • 深度学习已经使人工智能产生了强大的协同作用,促进了数字病理学领域的基于图像的诊断.
  • 基于人工智能的解决方案正在开发中,以提高诊断准确度和优化病理学家的时间.

研究的目的:

  • 讨论使人工智能在病理学中的整合成为可能的基础组件.
  • 检查AI在医疗行业当前的应用,特别是病理学.
  • 确定阻碍在病理学中采用人工智能的挑战和局限性.

主要方法:

  • 审查当前的人工智能技术及其与数字病理学的相关性.
  • 对AI在医学诊断中的应用现有文献的分析.
  • 探索用于图像分析的深度学习模型的集成.

主要成果:

  • 人工智能显示出有很大的潜力,可以帮助病理学家完成各种数字病理学任务.
  • 人工智能和深度学习之间的协同作用促进了基于图像的先进诊断.
  • 人工智能解决方案旨在减少诊断错误,提高病理学家的工作流效率.

结论:

  • 人工智能是医学病理学的变革性技术,提供了巨大的好处.
  • 成功实施需要解决当前的障碍和约束.
  • 提供了未来的建议,以有效地整合AI在病理学.