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大型语言模型会改变临床预测吗?

Yusuf Yildiz¹, Goran Nenadic², Meghna Jani³

¹Faculty of Biology, Medicine and Health, School of Health Sciences, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK. yusuf.yildiz@postgrad.manchester.ac.uk.

Diagnostic and prognostic research

|November 6, 2025

概括

大型语言模型 (LLM) 显示了使用电子健康记录改善临床预测模型的潜力. 然而,为了有效整合医疗保健,必须解决方法,验证,基础设施和监管方面的挑战.

科学领域:

人工智能在医学中的应用
医疗信息学医疗信息学
临床决策支持临床决策支持

背景情况:

大型语言模型 (LLM) 在医疗保健应用中越来越受欢迎.
临床预测模型 (CPM) 对于诊断和预后至关重要.
电子健康记录 (EHR) 包含丰富的纵向患者数据.

研究的目的:

评估LLM在增强CPM方面的潜力.
评估LLM在处理临床预测的纵向EHR数据方面的能力.
确定在医疗保健中LLM整合的挑战和机会.

主要方法:

对LLMs在临床预测中的应用进行审查和评论.
专注于LLM在处理多式联络和纵向EHR数据方面的能力.
对CPM的LLM实施现有挑战的分析.

主要成果:

在分析复杂的EHR数据以进行多结果预测方面,LLM表现有前途.
仍然存在重大挑战,包括时间到事件建模,预测校准和外部验证.
模型中的偏见和缺乏监管框架阻碍了广泛采用.

结论:

跨学科的合作对于公平有效地将LLMs纳入临床预测是必不可少的.
优先考虑时间意识,公平和可解释的模型是改变临床工作流程的关键.
解决方法和监管障碍对于实现医疗保健中的LLM潜力至关重要.

关键词:

这是一个偏见的偏见.临床预测模型的临床预测模型.公平的公平的公平的大型语言模型.监管和报告工作监管和报告工作

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