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细胞病理学的挑战和机遇人工智能人工智能

Meredith A VandeHaar¹, Hussien Al-Asi², Fatih Doganay²

¹Cytology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA.

Bioengineering (Basel, Switzerland)

|February 26, 2025

概括

人工智能 (AI) 为提高细胞病理学诊断和效率提供了巨大的潜力. 解决数据,算法和整合方面的挑战是实现AI的关键.

科学领域:

计算病理学计算病理学
医学成像分析医学成像分析
数字细胞学数字细胞学

背景情况:

人工智能 (AI) 对细胞病理学具有变革性的潜力.
当前的应用程序在数据,算法和工作流集成方面遇到障碍.
有机会提高准确性,效率和可访问性.

研究的目的:

审查AI在细胞病理学中的当前状态.
确定人工智能实施的关键挑战和机会.
强调克服障碍,改善患者护理的重要性.

主要方法:

关于AI在细胞病理学中的应用的综合文献综述.
分析挑战,包括数据质量,算法开发和临床验证.
探索机会,如提高准确性,效率和教育工具.

主要成果:

主要挑战包括有限的光学分割,数据采集复杂性,模型通用性和工作流集成.
显著的机会包括提高诊断准确度,减少观察者变化,并通过自动化提高效率.
人工智能可以作为教育工具,改善服务不足地区对诊断的准入.

结论:

解决数据,算法和整合方面的挑战对于在细胞病理学中成功采用人工智能至关重要.
利用人工智能的潜力可以导致更准确,更有效,更容易获得的细胞病理学诊断.
在细胞病理学中成功实施AI有望显著改善患者的治疗结果和护理.

关键词:

人工智能的人工智能是人工智能.细胞病理学细胞病理学深度学习是一种深度学习.在现场进行快速评估.

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