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超越准确性的推理:在诊断病理学中对大型语言模型的专家评估

Asim Waqas¹, Asma Khan², Zarifa Gahramanli Ozturk³

¹Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL.

medRxiv : the preprint server for health sciences

|April 29, 2025

概括

先进的AI语言模型在病理诊断方面表现有前途,Gemini和DeepSeek在推理质量方面表现优于其他人. 需要进一步发展,以解决临床整合的上下文理解和简洁性的局限性.

科学领域:

人工智能在医学中的应用
计算病理学计算病理学
医学诊断医学诊断医学诊断

背景情况:

诊断病理学依赖于综合各种数据类型的复杂推理.
这种认知过程的算法复制是一个重大挑战.
在病理学等专业医学领域评估大语言模型 (LLM) 的临床实用性至关重要.

研究的目的:

评估病理学诊断推理中的四个推理LLM的表现.
为了比较不同LLMs的语言质量和诊断推理策略.
评估观察者之间对得分一致性达成的协议.

主要方法:

四个推理的LLM在15个开放式的病理学问题上进行了测试.
病理学家通过语言质量和推理策略的结构化框架来评估反应.
统计分析,包括ANOVA和Tukey的HSD测试,用于模型比较.

主要成果:

与OpenAI模型相比,Gemini和DeepSeek的整体推理质量和分析深度都更高.
所有模型都显示出可比的准确性,但只有Gemini和DeepSeek始终运用专家式的推理策略.
双子座的观察者之间一致性最高,这表明它具有更大的一致性和可解释性.

结论:

像Gemini和DeepSeek这样的高级LLM可以模拟病理学中的专家级诊断推理,特别是在结构化方法中.
在上下文推理,启发式决策和一致性方面仍然存在局限性.
解决这些差距,并以简洁来平衡深度,对于在病理学中安全整合人工智能至关重要.

关键词:

人工智能评估临床推理临床推理生成性AI是一种人工智能.病理学病理学病理学推理大型语言模型

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