学术大语言模型对寻求健康信息的患者的可接受性
Saif Khairat1, John Geracitano1, Tianyi Niu1
1University of North Carolina at Chapel Hill.
Studies in health technology and informatics
|July 1, 2025
概括
一项试点研究评估了"SAM",一种用于医疗保健信息的本地大型语言模型 (LLM). 该研究发现,LLM易于学习,这表明改善患者对健康数据的参与的潜力.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 人与计算机的交互
背景情况:
- 大型语言模型 (LLM) 在医疗保健方面具有潜力,但面临着数据安全,准确性,偏见和可用性等挑战.
- 这些挑战可能会限制它们在帮助患者找到可靠的健康信息方面的有效性.
- 进行了一项试点研究,以评估当地开发的患者使用的LLM.
研究的目的:
- 评估一个名为"SAM"的本地大语言模型 (LLM) 的可用性,以获取健康信息.
- 在试点研究中确定有关LLM表现的关键主题和用户感知.
- 探索LLMs在克服患者信息搜索的医疗保健障碍方面的潜力.
主要方法:
- 一项试点研究涉及十名参与者与当地LLM"SAM" (基于Llama 7B架构) 进行互动.
- 参与者提交了五个领域的健康相关问题.
- 使用性被评估使用系统可用性量表 (SUS) 后的互动.
主要成果:
- 评分最高的可用性主题是学习的方便性,参与者强烈同意聊天机器人的快速学习能力 (平均值 = 4.7,SD = 0.46).
- 十分之八的参与者 (80%) 是女性.
- 这项研究提供了初步数据,关于用户对本地化LLM健康信息的体验.
结论:
- 为患者健康信息开发本地LLM需要解决医疗保健方面的重大障碍.
- "SAM"表现出易于学习,表明改善患者参与的潜力.
- 提高数据安全性,响应个性化和用户熟悉性对于优化LLM对患者结果的影响至关重要.
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