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[病理解剖学中的人工智能]

[Artificial intelligence in pathological anatomy].

作者信息

Solovev I A

机构信息

Pitirim Sorokin Syktyvkar State University, Syktyvkar, Russia.

出版信息

Arkh Patol. 2024;86(2):65-71. doi: 10.17116/patol20248602165.

Abstract

The review presents key concepts and global developments in the field of artificial intelligence used in pathological anatomy. The work examines two types of artificial intelligence (AI): weak and strong ones. A review of experimental algorithms using both deep machine learning and computer vision technologies to work with WSI images of preparations, diagnose and make a prognosis for various malignant neoplasms is carried out. It has been established that weak artificial intelligence at this stage of development of computer (digital) pathological anatomy shows significantly better results in speeding up and refining diagnostic procedures than strong artificial intelligence having signs of general intelligence. The article also discusses three options for the further development of AI assistants for pathologists based on the technologies of large language models (strong AI) ChatGPT (PathAsst), Flan-PaLM2 and LIMA. As a result of the analysis of the literature, key problems in the field were identified: the equipment of pathology institutions, the lack of experts in training neural networks, the lack of strict criteria for the clinical viability of AI diagnostic technologies.

摘要

这篇综述介绍了病理解剖学中使用的人工智能领域的关键概念和全球发展情况。该研究考察了两种类型的人工智能(AI):弱人工智能和强人工智能。对使用深度机器学习和计算机视觉技术处理标本的全切片图像(WSI)、诊断各种恶性肿瘤并进行预后分析的实验算法进行了综述。已经确定,在计算机(数字)病理解剖学发展的现阶段,弱人工智能在加快和完善诊断程序方面比具有通用智能迹象的强人工智能表现出明显更好的结果。本文还讨论了基于大语言模型(强人工智能)ChatGPT(PathAsst)、Flan-PaLM2和LIMA技术的病理学家人工智能助手进一步发展的三种选择。通过对文献的分析,确定了该领域的关键问题:病理机构的设备、缺乏训练神经网络的专家、缺乏人工智能诊断技术临床可行性的严格标准。

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