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大型语言模型在构建头部CT放射学报告中的比较性能:日本多机构验证研究

Hirotaka Takita¹, Shannon L Walston², Yasuhito Mitsuyama¹

¹Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-ku, Osaka, 545-8585, Japan.

Japanese journal of radiology

|May 14, 2025

概括

三种大型语言模型 (LLM) 在对内出血和骨骨折的日本放射学报告进行结构化时显示出高准确性. 克劳德的表现最好,提示策略显著影响诊断性能.

科学领域:

放射学中的人工智能
医疗报告的自然语言处理.
诊断准确性评估诊断准确性的评估

背景情况:

构建自由文本放射学报告对于数据分析和临床决策至关重要.
大型语言模型 (LLM) 提供了自动化这一过程的潜力,但其诊断性能需要评估.
提示策略可以显著影响LLM输出准确性.

研究的目的:

为了比较Claude,GPT和Gemini LLMs在构建日本头部CT报告中的诊断性能.
评估标准,思维链和自我一致性提示对LLM准确度对内出血和骨骨折的影响.
确定基于LLM的放射学报告结构的挑战和改进领域.

主要方法:

来自日本医学成像数据库 (2018-2023) 的3949个头部CT报告的回顾性分析.
由两名经过董事会认证的放射科医生为内出血和骨骨折建立的基本真理.
三个LLM (Claude,GPT,Gemini) 用三个提示策略进行了测试;通过准确性,精度,回忆和F1得分来衡量性能.

主要成果:

所有九个LLM提示符组合都表现出非常高的准确性.
克劳德在内出血和骨骨折 (p < 0.0001) 的准确性方面显著超过了GPT和双子座.
双子座的准确性随着思维链提示显著改善;常见的错误涉及模两可的短语和无关紧要的发现.

结论:

专有LLM显示出强大的潜力,用于构建内出血和头骨骨折的自由文本头部CT报告.
提示技术在优化LLM诊断性能方面发挥着至关重要的作用.
进一步的研究应该集中在改进提示和验证这些AI工具在未来的多语言临床环境中.

关键词:

自由文本放射学报告内出血内出血日本医疗成像数据库大型语言模型.头骨骨折骨折在头骨中结构化的放射学报告.

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