使用大型语言模型探索捐献体的沟通:准确性,可读性和伦理考虑
Fulya Temizsoy Korkmaz1, Fatma Ok1, Burak Karip1
1Department of Anatomy, Hamidiye Faculty of Medicine, University of Health Sciences, Istanbul, Turkey.
Anatomical sciences education
|September 7, 2025
概括
大型语言模型 (LLM) 可以为全身捐献产生准确和可读的教育材料. 像ChatGPT-4o和Grok3.0这样的顶级模型在补充捐助者通信,改善可访问性方面表现出有希望.
科学领域:
- 解剖学科学 解剖学科学
- 医学教育 医学教育
- 人工智能的人工智能
背景情况:
- 全身捐献的教育材料对于解剖学课程至关重要,但面临供应短缺.
- 现有的沟通方法可能无法满足对捐献器官准确,易于获取的信息的需求.
- 大型语言模型 (LLM) 提供了一个潜在的解决方案,以加强捐助者沟通和教育材料的创作.
研究的目的:
- 调查基于LLM的方法在为全身捐献产生有效的沟通材料时的有效性.
- 为LLM在这个领域的表现提供一个比较的定量基准和评估框架.
- 评估LLM生成的对体捐赠常见问题的答案的准确性,质量,可读性和词汇多样性.
主要方法:
- 五个LLM (ChatGPT-4o,Grok3.0,Claude4Sonnet,Gemini2.5 Flash,DeepSeekR1) 产生了土耳其人对六个常见的捐献问题的回答.
- 四位解剖学家根据准确性,质量,可读性和词汇多样性来评估生成的内容.
- 进行了统计分析,以比较LLM模型之间的性能差异.
主要成果:
- 在质量分数方面,ChatGPT-4o和Grok3.0显著优于其他模型 (平均检查清单分数分别为21.7 ± 2.8和21.0 ± 5.1).
- 表现最好的LLM在中学以下的阅读水平上制作了内容,这表明了高可读性.
- 通过提供可访问和可靠的信息,LLM产生的材料显示了促进全身捐献的潜力.
结论:
- 通过LLM制作的材料可以有效地补充全身捐赠信息,可能简化内容创作并减少员工的工作量.
- 伦理透明度,文化敏感性和持续的人类监督是LLM在敏感捐赠环境中使用的关键保障.
- 建议明确的治理框架,专家审计和披露的质量指标是负责的LLM在人体捐赠沟通中的实施.
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