简化放射学报告的大型语言模型:对患者,公众和临床医生的评估进行系统审查和元分析
Samer Alabed1, Abigail Anderson2, Ahmed Maiter1
1School of Medicine and Population Health, Institute for In Silico Medicine, National Institute for Health and Care Research, University of Sheffield, Sheffield, UK; Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK.
The Lancet. Digital health
|February 16, 2026
概括
大型语言模型 (LLM) 显著改善了患者对放射学报告的理解,使复杂的医疗信息更容易获得. 虽然一般准确,但在LLM简化报告中,只有很小一部分包含了临床上显著的错误.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 放射学通信 放射学通信
背景情况:
- 放射学报告经常使用患者无法访问的技术语言.
- 大型语言模型 (LLM) 展示了简化复杂文本的能力.
- 患者对医疗信息的理解对于明智的医疗保健决策至关重要.
研究的目的:
- 评估LLM在提高患者对放射学报告的理解方面的有效性.
- 评估LLM简化放射学报告的可读性和准确性.
- 确定基于LLM的简化在以患者为中心的沟通中的潜力.
主要方法:
- 使用LLM简化放射学报告的研究的系统审查和元分析.
- 纳入标准侧重于由公众或医疗专业人员评估的报告.
- 主要结局包括患者/临床医生的评估 (利克特分数) 和文本可读性指标.
主要成果:
- 分析了38项涉及12922份简化报告的研究.
- 患者评价LLM简化报告显著更容易理解 (平均利克特4.04比2.16).
- 可读性在各种模式 (CT,X射线,MRI) 中得到改善,Flesch-Kincaid等级水平较低.
- 临床医生对简化报告的准确性 (4.45) 和完整性 (4.53) 进行了高度评价.
- 观察到的小误差率为7.2%,其中0.9%具有临床意义.
结论:
- 基于LLM的简化大大提高了患者对放射学报告的理解和可读性.
- 临床医师的评估显示了高准确性和完整性,尽管有轻微的错误率.
- 在患者为中心的放射学沟通方面,LLM显示出前景,这就需要对临床影响进行进一步的研究.
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