使用大型语言模型来增强临床和健康人群的运动建议和体育活动:范围审查
Xiangxun Lai1, Jiacheng Chen2, Yue Lai3
1School of Sport Medicine and Rehabilitation, Beijing Sport University, No.48 Xinxi Road, Haidian District, Beijing, 100084, China.
JMIR medical informatics
|May 27, 2025
概括
大型语言模型 (LLM) 可以创建个性化的运动建议 (ER) 和体育活动 (PA) 计划,提高可访问性和参与度. 然而,专家验证对于安全有效地融入医疗保健至关重要.
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
- 运动科学 运动科学
背景情况:
- 定期运动建议 (ER) 和体力活动 (PA) 对慢性疾病管理至关重要.
- 开发个性化的炼计划需要大量的时间和专业知识.
- 大型语言模型 (LLM) 为创建个性化ER提供了潜在的解决方案,但它们的应用尚未开始.
研究的目的:
- 系统地审查和分类ER和PA中的LLM应用程序.
- 确定研究缺口和未来的方向,以在个性化卫生干预中整合LLM.
主要方法:
- 使用了一个范围审查方法.
- 在主要的科学数据库 (Web of Science,PubMed,IEEE,arXiv) 进行了搜索,截至2024年3月.
- 主题分析综合了来自11项纳入研究的发现,两名独立审查员选了论文.
主要成果:
- 现有的LLM,特别是基于ChatGPT的模型 (55%的研究),显示出在生成量身定制的ER方面有潜力.
- 研究表明,LLM可以节省临床时间,提高患者数据的安全性,促进参与,并支持行为改变.
- 通过LLM生成的PA指导提高了可访问性,特别是对于偏远或服务不足的人群.
结论:
- 在ER和PA方面,LLM是有前途的,但应该补充,而不是取代人类的专业知识.
- 专家验证对于确保安全和减轻与LLM使用相关的风险至关重要.
- 未来的研究应该专注于试点测试,临床医师培训和大规模试验,以实现道德和有效的整合.
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