评估LLM在为常规放射学程序的知情同意过程中支持患者教育方面的作用
Eric Einspänner1, Roland Schwab1, Sebastian Hupfeld2
1Otto von Guericke University Magdeburg Medical Faculty, Clinic for Neuroradiology, Magdeburg, DE.
The British journal of radiology
|September 19, 2025
概括
像GPT-4这样的较新的大型语言模型 (LLM) 在回答放射病患者问题时显示出更高的准确性. 然而,它们的有效性因成像方式和问题的复杂性而异,特别是在临床调查中.
科学领域:
- 人工智能在医学中的应用
- 放射学 患者教育 患者教育
- 自然语言处理自然语言处理.
背景情况:
- 知情同意对于患者了解医疗程序至关重要.
- 大型语言模型 (LLM) 为患者教育提供了潜力.
- 在医疗保健环境中评估LLM绩效是必不可少的.
研究的目的:
- 评估三个LLM聊天机器人 (GPT-3.5-turbo,GPT-4-turbo,GPT-4o) 在回答CT,MRI和DSA知情同意的患者问题的有效性.
- 评估LLM产生的答案的准确性和清晰度.
主要方法:
- 两位放射科医生制定了90个一般,临床和技术问题,用于CT,MRI和DSA知情同意.
- 每个LLM都回答了五次问题.
- 放射科医生和医学物理学家使用利克特尺度对医疗和技术准确性和清晰度的反应进行了评分.
- 语义相似性使用SBERT和共弦相似性进行了分析.
主要成果:
- 从医生和物理学家来看,GPT-4模型的评分明显高于GPT-3.5.
- 医生对复杂的模式 (DSA,MRI) 评价GPT-4模型较低,这一趋势在物理学家的评价中没有见过.
- 斯伯特的分析表明,所有模型的内部一致性都很高.
结论:
- 士有效地回答一般和技术放射学问题,但在复杂的临床调查中扎.
- 较新的LLM版本优越,但性能取决于模式,临床和技术专家对其的看法不同.
- LLM显示出增强放射学知情同意的潜力,性能因模型和模式而异.
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