在解释放射学报告中对大型语言模型的比较评估:可读性,可理解性和通信特征的专家评估
Ahmet Bozer1, Yeliz Pekçevik2,3
1Department of Radiology, Ministry of Health Izmir City Hospital, Izmir, Turkey. drahmetbozer@gmail.com.
Insights into imaging
|October 30, 2025
概括
在测试的AI模型中,ChatGPT提供了最易于阅读和理解的放射学报告解释. 双子提供了更好的患者指导,而Copilot包含了更多的临床细节和谨慎的语言,建议使用特定情境的AI.
科学领域:
- 放射学中的人工智能
- 在医疗保健中的自然语言处理.
- 医学沟通 医学沟通
背景情况:
- 放射学报告包含复杂的医疗信息.
- 患者经常难以理解他们的放射学报告.
- 大型语言模型 (LLM) 提供了改善患者理解的潜力.
研究的目的:
- 为了比较放射学报告的可理解性,可读性和通信特性,使用ChatGPT,Gemini和Copilot生成的解释.
- 评估医学正确性,患者指导和LLM生成的解释的焦虑诱导潜力.
- 在放射性通信中为AI的最佳使用提供信息.
主要方法:
- 分析了来自五个子专业的100份匿名放射学报告.
- 报告由ChatGPT (GPT-3.5),Gemini和Copilot使用标准化提示符来解释.
- 答案被评估为医疗正确性,可理解性 (PEMAT-U),可读性 (FRE,ARI,GFI) 和沟通特征.
主要成果:
- 所有模型都实现了高医疗正确性 (平均1.97/2).
- 聊天GPT提供了最易于阅读和理解的解释 (p < 0.01).
- 双子座在患者指导方面表现出色,副驾驶员在不确定性语言和临床建议方面表现出色.
结论:
- 聊天GPT在放射学报告解释方面表现出卓越的可读性和可理解性.
- 人工智能模型输出中的差异支持用于增强患者沟通的特定环境应用.
- 这些发现指导了人工智能工具的战略实施,以改善患者对放射性发现的参与.
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