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面向病理学家的简化人工智能术语

Simplified Artificial Intelligence Terminology for Pathologists.

作者信息

Zabihollahy Fatemeh, Mankaruos Michael, Mohareb Maxim, Youssef Timothy, Soleymani Yasaman, Yousef George M

机构信息

Laboratory Medicine Program, University Health Network, Toronto, ON M5G 2C4, Canada.

Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5G 2C4, Canada.

出版信息

Diagnostics (Basel). 2025 Jul 3;15(13):1699. doi: 10.3390/diagnostics15131699.

Abstract

The expanding shift towards digital pathology in clinical practice globally highlights its potential to enhance patient care through artificial intelligence (AI)-powered, computer-assisted diagnostics. Effective communication between AI scientists and pathologists is crucial for this transformation, though their differing technical languages can pose challenges. The manuscript aims to offer simplified explanations of common AI terminology, along with practical examples and illustrations, to help pathologists better grasp AI concepts. This review is divided into the following sections: AI technologies and algorithms in computational pathology; frameworks for training AI models; nomenclature of image analysis; and public datasets for computational pathology research. These sections collectively provide a comprehensive understanding of the current landscape and resources in computational pathology. The manuscript fosters better communication between these fields and showcases the advantages of AI technologies in pathology.

摘要

全球临床实践中向数字病理学不断扩展的转变凸显了其通过人工智能(AI)驱动的计算机辅助诊断来改善患者护理的潜力。人工智能科学家和病理学家之间的有效沟通对于这一转变至关重要,尽管他们不同的技术语言可能会带来挑战。本文旨在提供常见人工智能术语的简化解释,并配以实际例子和插图,以帮助病理学家更好地理解人工智能概念。本综述分为以下几个部分:计算病理学中的人工智能技术和算法;训练人工智能模型的框架;图像分析的命名法;以及用于计算病理学研究的公共数据集。这些部分共同提供了对计算病理学当前格局和资源的全面理解。本文促进了这些领域之间的更好沟通,并展示了人工智能技术在病理学中的优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f513/12248457/46e6ae28c2e9/diagnostics-15-01699-g001.jpg

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