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医学图像分析中的大型语言模型:系统调查和未来方向

Bushra Urooj¹, Muhammad Fayaz², Shafqat Ali³

¹Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea.

Bioengineering (Basel, Switzerland)

|August 28, 2025

概括

大型语言模型 (LLM) 在医疗图像分析中表现有前途,用于报告生成和分类等任务. 这项调查详细介绍了它们的应用,数据稀缺等挑战,以及多式联络学习等未来方向.

科学领域:

人工智能
医学成像
自然语言处理

背景情况:

大型语言模型 (LLM) 越来越多地被整合到医学图像分析中.
它们的应用包括报告生成,疾病分类,视觉问题答案和细分.
提供一种新的方法来解释复杂的多式联络医疗数据.

研究的目的:

在医学图像分析中调查和汇编LLM的已知应用.
突出与LLM实施相关的潜在好处和关键挑战.
概述未来的研究方向,并为研究人员和从业人员提供结构化的指导.

主要方法:

引入X阶段调整框架用于LLM微调 (零,一阶段和多阶段).
对医学图像分析中的LLM现有文献进行全面审查.
讨论包括数据稀疏性,输出幻觉,隐私和知识更新在内的挑战.

主要成果:

在医疗图像分析任务中,LLM表现出各种能力.
基于任务的复杂性和数据的可用性,X阶段的调整框架提供了精细调整LLM的结构化方法.
需要进一步研究和创新解决方案.

结论:

在医疗图像分析方面,LLM提供了显著的机会.
解决数据短缺和隐私等挑战对于广泛采用至关重要.
未来的工作应集中在与决策支持系统,多模式学习和联合学习的整合上.

关键词:

疾病分类大型语言模型医学图像分析医疗报告的生成多模式学习在视觉上回答问题 x阶段调整

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