儿童放射瘤学治疗前教育的大型语言模型:一项比较评估研究
Dominik Wawrzuta1, Aleksandra Napieralska2,3,4, Katarzyna Ludwikowska1
1Department of Radiation Oncology, Maria Sklodowska-Curie National Research Institute of Oncology, Wawelska 15B, 02-034 Warsaw, Poland.
Clinical and translational radiation oncology
|January 27, 2025
概括
大型语言模型 (LLM) 可以帮助儿科放射瘤患者和家长找到可靠的预治疗信息. GPT-4的质量与瘤学家的质量相当,但由于潜在的不准确性,GPT-3.5模型需要谨慎使用.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医学教育 医学教育
背景情况:
- 儿科放射治疗患者和家长在初始咨询之前经常在网上寻找信息.
- 不可靠的在线来源对患者的理解和准备构成风险.
- 大型语言模型 (LLM) 为可访问,可靠的预治疗教育提供了潜在的解决方案.
研究的目的:
- 评估生成预训练变压器 (GPT) 对儿科放射瘤学问题的反应质量.
- 将GPT模型与专家准备的答案进行比较,以确保可靠性,简洁性和易懂性.
主要方法:
- 收集了儿科患者和家长的治疗前放射治疗问题.
- 使用GPT-3.5,GPT-4和微调的GPT-3.5.5生成响应.
- 专家放射瘤学家在盲目的多机构环境中审查了反应.
主要成果:
- GPT-4和放射瘤学家提供了最高质量的反应.
- GPT-4的反应有时过于多言;微调的GPT-3.5往往过于简单.
- 不充分的反应很少 (4%),GPT-3.5模型是主要来源.
结论:
- 在儿科放射瘤学中,LLM可以成为有价值的教育工具.
- 尽管偶尔会出现多言多语和错误,但GPT-4在提供瘤学家级信息方面表现有前途.
- 由于不充分反应的可能性更高,GPT-3.5模型需要谨慎使用.
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