开源和专有大型语言模型在生成患者友好的放射学胸部CT报告中的性能
Philipp Prucker1, Felix Busch1, Felix Dorfner2
1Department of Diagnostic and Interventional Radiology, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany.
Clinical imaging
|July 10, 2025
概括
像Llama-3-70b这样的开源大型语言模型 (LLM) 显示出创建患者友好的放射学报告的巨大潜力,在质量和可读性方面与专有模型竞争. 进一步开发可以提高医疗成像中的患者沟通.
科学领域:
- 医疗成像中的人工智能
- 在医疗保健中的自然语言处理.
- 放射学报告 放射学报告
背景情况:
- 大型语言模型 (LLM) 提供了简化复杂医疗信息的潜力.
- 评估开源与专有LLM对患者友好的放射学报告的性能至关重要.
- 胸部计算机断层扫描 (CT) 报告通常包含患者无法访问的技术术语.
研究的目的:
- 为了比较开源和专有LLM在从胸部CTs中生成患者友好的放射学报告的能力.
- 用定量可读性指标和放射科医生的定性评估来评估LLM的表现.
主要方法:
- 50份胸部CT报告由7个LLM处理 (3个开源,4个专有).
- 量化可读性使用五个指标进行评估;定性评估涉及三名放射科医生对患者友好性进行评分.
- 计数了内容和连贯性错误,并统计分析了评审者之间的可靠性.
主要成果:
- 在大多数质量方面,Llama-3-70b在专有模型上表现出非劣势.
- GPT-3.5-Turbo获得了最好的整体可读性得分,而Llama-3-70b在CLI指标上表现出色.
- 与Llama-3-70b相比,Claude-3-Opus和GPT-4显示出高质量的评分和完美的事实准确性,以及更高的情感敏感性.
结论:
- 开源的LLM,特别是Llama-3-70b,显示出在生成高质量,患者友好的放射学报告方面有很大的前景.
- 进一步适应开源LLM可以显著推进以患者为中心的医疗报告技术.
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