干预性放射学手术前患者教育的大型语言模型:一项比较研究
Bogdan Levita1, Semil Eminovic2, Willie Magnus Lüdemann2
1Department of Radiology, Charité - Universitätsmedizin Berlin, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany. bogdan.levita@charite.de.
CVIR endovascular
|October 13, 2025
概括
大型语言模型DeepSeek-V3和ChatGPT-4o在回答 TAPE,BEST 和 CT-HDR 胸膜疗法等干预性放射学程序的患者问题方面显示出希望. 然而,其他LLM的表现不佳,表明它们还不能取代临床咨询.
科学领域:
- 干预性放射学 干预性放射学
- 人工智能在医学中的应用
- 医学教育 医学教育
背景情况:
- 患者对复杂的干预放射学程序的理解对于知情同意和遵守至关重要.
- 大型语言模型 (LLM) 提供了一个潜在的工具来增强患者教育和简化临床工作流程.
- 评估LLM在回答与TAPE,CT-HDR支臂疗法和BEST等手术相关的患者特定问题的表现是必不可少的.
研究的目的:
- 评估四种LLM (ChatGPT-4o,DeepSeek-V3,OpenBioLLM-8b,BioMistral-7b) 的准确性和适用性,以响应患者关于跨动脉周关节栓塞 (TAPE),CT导向高剂量率胸膜疗法 (CT-HDR) 和白素电硬化疗法 (BEST) 的疑问.
- 确定这些LLM在干预放射学环境中提高患者理解和临床效率的潜力.
- 确定与使用LLM用于患者教育相关的风险和局限性.
主要方法:
- 总共有105个与患者相关的问题 (35个TAPE,34个CT-HDR,36个BEST) 被输入到选定的LLMs中.
- 两位经过董事会认证的放射科医生使用5分利克尔特尺度对患者教育的准确性和适宜性进行了评估.
- 进行了统计分析,以比较不同问题类别和程序的LLM绩效.
主要成果:
- DeepSeek-V3和ChatGPT-4o表现出很高的性能,在TAPE相关问题上获得了可比得分,并在BEST和CT-HDR支臂疗法中表现出色.
- 在所有评估的程序中,OpenBioLLM-8b和BioMistral-7b的性能显著降低.
- 在准备/计划问题类别的程序之间没有观察到统计学上显著的绩效差异.
结论:
- DeepSeek-V3和ChatGPT-4o显示出在干预放射学中提高患者教育的巨大潜力,特别是对于复杂的程序,如TAPE,BEST和CT-HDR支臂疗法.
- 目前OpenBioLLM-8b和BioMistral-7b的局限性和不准确性凸显出LLM不能完全取代全面的临床咨询.
- 建议通过患者反和临床实施研究进行进一步验证,以确认LLM在这个领域的实用性和安全性.
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