数字时代的血液病理学实践:有什么变化?
1Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, USA.
International journal of laboratory hematology
|August 14, 2025
概括
数字病理学和人工智能 (AI) 可以增强血液病理学工作流程,用于血液细胞计数和组织分析. 对于AI在临床实践中的实施,必须解决监管和数据标准化等挑战.
科学领域:
- 血液病理学 血液病理学
- 数字病理学数字病理学
- 人工智能的人工智能
背景情况:
- 血液病理学工作流程涉及诊断和患者管理的复杂数据,从血液细胞计数和外周血液 (PB) 和骨髓 (BM) 的形态评估开始.
- 数字病理学有可能通过人工智能辅助和自动化评估来彻底改变PB和BM评估.
研究的目的:
- 审查血液病理学领域数字化现状.
- 讨论最近使用机器学习进行自动化样本分析的研究.
- 概述人工智能实施的优势和障碍,并提出未来人工智能驱动的工作流程.
主要方法:
- 关于数字病理学和血液病理学AI的当前文献的综述.
- 机器学习模型的分析用于血液和骨髓样本的自动化分析.
- 讨论实施的挑战和未来的工作流程的发展.
主要成果:
- 数字病理学和人工智能显示出改善血液病理学诊断的希望.
- 包括监管监督,数据标准化和劳动力培训在内的重大障碍阻碍了广泛采用.
- 人工智能驱动的工作流可以提高临床工作的效率和全面性.
结论:
- 数字化和人工智能对血液病理学实践具有变革性的潜力.
- 克服实施障碍对于实现AI在临床环境中的好处至关重要.
- 整合人工智能的未来工作流可以带来更高效,更准确的患者管理.
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