紧急问题:评估与皮罗尼病相关的大型语言模型聊天机器人响应
Christopher J Warren1, Victoria S Edmonds1, Nicolette G Payne1
1Department of Urology, Mayo Clinic Arizona, Phoenix, AZ 85054, United States.
Sexual medicine
|September 11, 2024
概括
大型语言模型 (LLM) 显示了教育患者关于皮罗尼病 (PD) 的潜力,但需要提醒质量. 他们的阅读水平太高,引文不可靠,需要用户教育.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 患者越来越多地在网上寻找健康信息,特别是对于像皮罗尼病 (PD) 这样的敏感疾病.
- 大型语言模型 (LLM) 提供对话式AI功能,具有患者教育的潜力.
- 目前尚不清楚LLM生成的PD内容的准确性和可读性.
研究的目的:
- 评估有关PD的LLM答案的质量和可读性.
- 为了确定用户提示是否提高了LLM响应质量.
- 评估LLM对模拟患者查询的响应的准确性,完整性和可读性.
主要方法:
- 四名LLM被询问了国家卫生研究院的非提示和提示的PD常见问题.
- 通过使用DISCERN问卷来评估答案质量.
- 准确性和完整性由独立审稿人使用利克特等级来评估.
主要成果:
- 促进从中等到高的LLM信息质量显著提高.
- 在LLM中,高准确度 (5.5/6.0) 和完整性 (2.8/3.0) 均较高.
- 平均阅读水平为12.9年级;聊天机器人无法生成8年级级别的文本,引用的准确率只有42.5%.
结论:
- 在适当的提示和人类监督下,LLM可以对PD患者教育有价值.
- 目前的LLM可读性超过了患者的平均理解能力,引文缺乏可靠性.
- 教育患者和医生关于LLM互动对于有效使用和潜在的倦怠减轻至关重要.
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