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基于人工智能的脏移植活检病理诊断的前景

Kazuhiro Iwadoh¹, Makoto Tonsho²

¹Department of Transplant Surgery, Mita Hospital, International University of Health and Welfare, Minato, Japan, kiwadoh@gmail.com.

|October 18, 2025

概括

人工智能使用深度神经网络和大型语言模型正在改变数字病理学. 将人工智能集成到移植病理学中可以标准化诊断并减少病理学家的工作量.

科学领域:

人工智能 (AI) 和数字病理学 (DP) 的进展.
深度神经网络 (DNN) 和大型语言模型 (LLM) 在医学诊断中的应用.

背景情况:

在最初的挫折之后,人工智能开发在DNN和LLM方面取得了显著的进展,这些进展是由非线性模型和自主学习驱动的.
DNN擅长图像分类,而LLM则基于上下文理解生成文本.

研究的目的:

突出全球数字病理学的进步,并确定日本在采用数字病理学的滞后.
强调将DP纳入移植病理学 (RTP) 的必要性,以实现诊断标准化和减轻工作量.
讨论人工智能在提高诊断客观性和病理学家支持方面的潜力.

主要方法:

在图像分类 (DNNs) 和文本生成 (LLMs) 中对AI能力的审查.
对人工智能在数字病理学方面的影响进行分析,参考CAMELYON16挑战.
检查监管批准 (FDA) 和法律框架 (美国) 的AI在诊断.
讨论诸如班夫数字病理学工作组 (Banff Digital Pathology Working Group) 等关于标准化的倡议.

主要成果:

人工智能证明了与专家病理学家可比的诊断准确性.
美国FDA批准的十多个DP系统用于美国的初级诊断.
美国法律规定了人工智能相关误诊的共同责任,鼓励准确性和保护.
班夫数字病理学工作组促进仓库建设,算法共享和标准化.

结论:

在RTP中集成人工智能提供了增强的诊断客观性和减少病理学家的工作量.
与班夫分类更新一致的持续人工智能再培训确保了全球标准化的DP.
成功的高级DP实施需要移植病理学家,AI工程师和GPU资源之间的合作.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.数字病理学数字病理学大型语言模型.移植脏的活检

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