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基于人工智能的基础模型和"副驾驶员"在癌症病理学中的作用:潜力和挑战

Cillian H Cheng¹, Chi Chun Wong²

¹Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.

Journal of experimental & clinical cancer research : CR

|November 29, 2025

概括

人工智能 (AI) 正在彻底改变癌症病理学,从特定任务的算法转向多功能基础模型 (FMs),以改善诊断. 对于广泛的临床采用,验证,可解释性和公平性仍然存在挑战.

科学领域:

数字病理学数字病理学
计算病理学计算病理学
人工智能在医学中的应用

背景情况:

全球病理学家短缺和不断增加的诊断复杂性需要先进的解决方案.
人工智能 (AI) 在病理学中的整合为解决这些挑战提供了一个有希望的途径.
该领域正在迅速发展,从特定任务的算法到更通用的基础模型 (FMs).

研究的目的:

审查人工智能的演变和影响,特别是基础模型,在癌症病理学中.
突出FM在分类,亚型,结果预测和生物标志物发现方面的能力.
确定挑战,并为AI在病理学中的临床转化提出解决方案.

主要方法:

对癌症病理学AI近期进展的审查.
基础模型 (例如UNI,CONCH,GigaPath,mSTAR,Atlas) 和人工智能副驾驶员 (例如PathChat,SmartPath) 的分析.
讨论培训方法,包括在大型数据集上进行自我监督和多模式学习.

主要成果:

基础模型在各种癌症病理学任务中表现出显著的能力.
人工智能副驾驶员显示出对简化诊断工作流程的潜力.
关键的挑战包括不良的概括性,缺乏可解释性,产生人工智能幻觉和公平性问题.

结论:

基金会模型代表了癌症病理学AI的范式转变.
解决验证,可解释性和监管障碍对于临床实施至关重要.
负责任地开发和部署人工智能有望提高诊断准确性,效率和患者的结果.

关键词:

人工智能"副驾驶员"人工智能 (AI) 是一种人工智能.癌症病理学癌症病理学基金会模型基金会模型培训和验证培训和验证

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