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¹Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA sean.hacking@nyulangone.org.

Journal of clinical pathology

|May 12, 2025

概括

基础模型是一种人工智能 (AI),正在改变病理诊断和工作流程. 这些先进的AI系统改善了癌症亚型和生物标志物识别,使病理学更有效和更容易获得.

科学领域:

计算病理学计算病理学
医学中的人工智能
数字病理学数字病理学

背景情况:

基础模型是大规模的,预训练有素的人工智能 (AI) 系统,彻底改变了病理学.
它们解决了分析千兆像素全幻灯片图像的计算挑战.
这些模型增强了诊断,自动化工作流程,并扩大了病理学中的应用.

研究的目的:

探索基础模型在计算病理学的影响和应用.
为突出AI在癌症亚型,生物标记物识别和图像分析方面的进展.
讨论开源模型和分散式学习在促进病理学AI的作用.

主要方法:

使用像GigaPath这样的架构来进行千兆像素图像分析.
采用视觉语言模型 (例如,CONCH) 来整合组织病理学图像与生物医学文本.
实施分散式学习方法,如联合和群体学习,以确保安全,大规模的培训.

主要成果:

在癌症亚型和生物标志物识别方面实现最先进的性能.
允许高效的文本到图像检索和分类,最小的微调.
促进安全,大规模的人工智能模型培训,而不影响患者的机密性.

结论:

基础模型是计算病理学的变革力量,桥梁AI创新和临床实施.
开源模型和去中心化的学习增强了可访问性,协作和数据隐私.
解决可扩展性,偏差缓解和临床决策调整方面的挑战对于未来的进步至关重要.

关键词:

人工智能的人工智能机器学习机器学习神经网络,计算机,计算机网络

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