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生成型大语言模型与同行患者的答案质量,用于解释实验室测试结果的客户:评估研究

Zhe He¹, Balu Bhasuran¹, Qiao Jin²

¹School of Information, Florida State University, Tallahassee, Florida, USA.

|March 26, 2024

概括

大型语言模型 (LLM) 可以回答患者关于实验室结果的问题. 与其他LLM和人类答案相比,GPT-4显示了最准确,有用,相关和安全的响应.

科学领域:

医疗信息学医学信息学
医疗保健中的人工智能

背景情况:

患者对电子健康记录和实验室结果的访问正在增加.
患者经常发现实验室结果令人困惑,并在网上寻求同行建议.
来自社交网站问答网站的信息质量和可靠性差异很大.

结论:

与其他LLM和人类答案相比,GPT-4在相关性,正确性,帮助性和安全性方面表现优越.
法学学士学位的答案可能缺乏医学上下文的解释,包含不准确性,或省略引用.
像快速工程和检索增强生成这样的增强方法可以提高LLM响应质量.

关键词:

聊天GPT 聊天在GPT 聊天生成性AI是一种人工智能.实验室测试结果实验室测试结果大型语言模型.自然语言处理自然语言处理.患者教育患者教育

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