Syrykh Charlotte, van den Brand Michiel, Kather Jakob Nikolas, Laurent Camille
Department of Pathology, Institut Universitaire du Cancer-Oncopole de Toulouse CHU Toulouse, Toulouse, France.
Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
Histopathology. 2025 Jan;86(1):58-68. doi: 10.1111/his.15327. Epub 2024 Oct 22.
The advent of digital pathology and the deployment of high-throughput molecular techniques are generating an unprecedented mass of data. Thanks to advances in computational sciences, artificial intelligence (AI) approaches represent a promising avenue for extracting relevant information from complex data structures. From diagnostic assistance to powerful research tools, the potential fields of application of machine learning techniques in pathology are vast and constitute the subject of considerable research work. The aim of this article is to provide an overview of the potential applications of AI in the field of haematopathology and to define the role that these emerging technologies could play in our laboratories in the short to medium term.
数字病理学的出现以及高通量分子技术的应用正在产生前所未有的海量数据。得益于计算科学的进步,人工智能(AI)方法成为从复杂数据结构中提取相关信息的一条有前景的途径。从诊断辅助到强大的研究工具,机器学习技术在病理学中的潜在应用领域广阔,并且构成了大量研究工作的主题。本文旨在概述人工智能在血液病理学领域的潜在应用,并确定这些新兴技术在短期内至中期内在我们实验室中可能发挥的作用。