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疾病诊断的大型语言模型:范围审查

Shuang Zhou¹, Zidu Xu², Mian Zhang³

¹Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN USA.

NPJ artificial intelligence..

|July 3, 2025

概括

大型语言模型 (LLM) 显示出自动疾病诊断的前景. 本综述提供了LLM在诊断中的应用的全面概述,涵盖疾病,数据,技术和评估方法.

科学领域:

人工智能在医学中的应用
临床信息学临床信息学
计算病理学计算病理学

背景情况:

自动疾病诊断对于临床实践至关重要.
大型语言模型 (LLM) 代表了人工智能的重大进步,在诊断应用中展示了潜力.
目前缺乏对LLM在疾病诊断中的利用的全面理解.

研究的目的:

对基于LLM的疾病诊断方法进行现有文献的全面审查.
分析LLM在不同疾病类型,临床专业和数据模式中的应用.
识别和评估用于诊断任务的LLM技术和评估策略.

主要方法:

对使用LLM用于疾病诊断的研究进行系统性文献综述.
根据疾病特征,临床数据类型,LLM架构和评估指标对审查的研究进行分类.
分析当前研究领域的趋势,差距和局限性.

主要成果:

识别LLM在多个临床专业诊断各种疾病中的多种应用.
用LLM用于诊断目的的临床数据类型的表征.
在该领域普遍存在的LLM技术和评估方法的摘要.

结论:

在提高自动疾病诊断方面,LLM具有显著的潜力.
提供了针对临床诊断环境中有效应用和严格评估LLM的建议.
概述了未来的研究方向,以解决当前的局限性并推动该领域的发展.

关键词:

计算模型是计算模型.计算机科学计算机科学

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