对个人睡眠健康中的大型语言模型进行广泛的审查
1From the Department of Computer Science, University of Manitoba, Winnipeg, Canada.
Mayo Clinic proceedings. Digital health
|December 11, 2025
概括
大型语言模型通过数字工具提供个性化的睡眠指导. 这份对21篇论文的综述显示了它们在非临床睡眠健康应用中的潜力,尽管证据仍然是初步的.
科学领域:
- 数字健康数字健康
- 医疗保健中的人工智能
- 睡眠科学 睡眠科学
背景情况:
- 越来越多地强调睡眠健康对幸福的作用.
- 越来越多地使用数字工具来管理睡眠.
- 大型语言模型 (LLM) 的出现,用于个性化的数字健康应用.
研究的目的:
- 为非临床,以用户为中心的睡眠健康应用综合LLM的研究.
- 确定这个新兴领域的核心用例,数据源和方法.
- 检查人工智能驱动睡眠支持的当前能力,挑战和未来机会.
主要方法:
- 对21篇学术论文进行了范围调查.
- 将LLM应用分为四个用例:教育,特定条件的支持,个性化指导和基于CBT的自我帮助.
- 分析数据源,模型架构,微调,个性化策略和评估框架.
主要成果:
- 法律学士课程被探索为教育性问答,睡眠呼吸暂停支持,个性化辅导和CBT自助.
- 使用多种数据源 (可穿戴设备,自我报告,合成数据) 和个性化策略.
- 评估方法从专家评价到试点用户研究,以及初步发现,各不相同.
结论:
- 在非临床环境中,LLM显示出个性化,对话式睡眠指导的前景.
- 方法方面的挑战包括数据多样性,个性化技术和强有力的评估.
- 需要进一步的研究来确定外部有效性和通用性,因为目前的证据是初步的.
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