通过人工智能增强以患者为中心的护理:对神经内分泌瘤查询问题的反应的研究
Francesco Panzuto1, Alessandro Gallo2, Marco Marini3
1Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome. Digestive Disease Unit, Sant'Andrea University Hospital, ENETS Center of Excellence, Rome, Italy. Francesco.panzuto@uniroma1.it.
Endocrine
|June 5, 2025
概括
大型语言模型 (LLM) 显示了神经内分泌瘤 (NEN) 管理的潜力,提供了明确但有时不准确的信息. 专家监督对于将这些AI工具整合到患者护理中至关重要.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越多地被采用,但它们在神经内分泌瘤 (NENs) 中的实用性仍未得到检查.
- 这项研究解决了理解LLM对NEN管理的表现的差距.
研究的目的:
- 严格评估LLM生成对关键NEN管理问题的答案的准确性,清晰性和完整性.
- 评估LLM作为NENs临床瘤学环境中的工具的潜力.
主要方法:
- 对三个领先的LLM (ChatGPT Plus,Copilot,Perplexity) 提出了9个NEN管理问题.
- 22名专家医生和24名NEN患者对答案的准确性,清晰性和相关性进行了评分.
- 一个线性混合效应模型分析了LLM的表现和影响因素.
主要成果:
- 聊天GPT Plus获得了最高的整体得分 (3.72),其次是 Copilot (3.54) 和 Perplexity (3.22).
- 在所有LLM中,清晰度的评分始终高于准确性和完整性.
- 关于化疗指示的问题得分最低,这表明复杂的临床决策存在挑战.
结论:
- 在NEN管理中,LLM展示了其作为信息资源的潜力,提供了明确的,虽然可变的响应.
- 持续的发展和临床医生监督对于LLMs在患者沟通和瘤实践中安全有效地整合至关重要.
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