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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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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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[诊断中的人工智能-病理学的视角]

Stefan Schulz1, Moritz Jesinghaus2, Sebastian Foersch3

  • 1Institut für Pathologie, Universitätsmedizin Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland.

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人工智能 (AI) 正在通过视觉转换器和基础模型等先进算法改变病理学,提高诊断准确性. 虽然人工智能显示出临床整合的前景,但在组织病理学中广泛采用仍然存在挑战.

关键词:
在病理学中的人工智能.支持决定的决定支持.诊断的人工智能生物标志物数字病理学数字病理学数字化转型数字化转型多式联运数据集成是多式联运数据集成.模式识别 模式识别 模式识别

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科学领域:

  • 数字病理学和计算分析
  • 医学中的人工智能.
  • 组织病理学和诊断工作流程

背景情况:

  • 瘤诊断和治疗越来越复杂,对传统的病理学构成挑战.
  • 人工智能 (AI) 正在迅速发展,成为数字医学不可或缺的一部分.
  • 数字全幻灯片图像 (WSIs) 的普及,自2019年以来推动了数字病理学研究的指数式增长.

研究的目的:

  • 审查数字病理学人工智能算法的最新进展.
  • 评估AI在病理学中的过渡,从概念验证到临床实施.
  • 确定基础模型和视觉语言模型 (VLMs) 对基因病学诊断的潜在影响.

主要方法:

  • 审查人工智能算法的最新创新,包括卷积神经网络 (CNN),视觉转换器 (ViTs) 和基础模型.
  • 对数字病理学越来越多的出版物进行分析.
  • 检查AI算法在组织病理学中的监管批准.
  • 探索新兴技术,如视觉语言模型 (VLMs).

主要成果:

  • 基于人工智能的解决方案显示出提高诊断工作流程效率和灵敏性的潜力.
  • 基础模型由于其可通用性和广泛适用性而迅速变得越来越重要.
  • 视觉语言模型 (VLMs) 可实现图像和文本数据的多式集成,用于交互式诊断.
  • 几种用于组织病理学的人工智能算法在美国和欧洲获得了监管批准.

结论:

  • 数字病理学领域正在从概念验证研究转向临床实施.
  • 基础模型已经准备好显著重塑组织病理学诊断.
  • 克服技术,法律和社会心理障碍对于人工智能在病理学中的广泛临床应用至关重要.