基于大型语言模型的运动和健康辅导模型的评估策略:范围审查
Xiangxun Lai1,2, Yue Lai3, JiaCheng Chen1
1Research and Communication Center for Exercise and Health, Xiamen University of Technology, 600 Ligong Road, Jimei District, Xiamen, Fujian Province, 361024, China, 86 15606951380.
Journal of medical Internet research
|October 14, 2025
概括
目前对基于大型语言模型 (LLM) 的AI健康教练的评估是分散的,缺乏严格性. 制定标准化验证框架对于在运动和健康方面安全有效的AI辅导至关重要.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 运动科学 运动科学
背景情况:
- 基于大型语言模型 (LLM) 的AI教练为个性化健康和运动干预提供了潜力.
- 目前对这些人工智能教练的评估方法是分散的,缺乏安全和实时反的标准化框架.
研究的目的:
- 系统地绘制和分析目前对基于LLM的AI教练在运动和健康方面的评估策略.
- 确定这些人工智能辅导工具的优势,局限性,并提出针对这些人工智能辅导工具的强大,标准化验证的方向.
主要方法:
- 根据PRISMA-ScR指南进行了范围审查,搜索了6个主要数据库.
- 包括的研究明确报告了基于LLM的炼和健康指导的评估方法.
- 为了评估方法的深度,开发了一个5分评价严格度评分 (ERS).
主要成果:
- 包括20项研究,主要使用专有LLM如ChatGPT (75%).
- 评估策略异质,结合了人类评级 (80%) 和自动化指标 (40%).
- 方法严谨性低,平均ERS为2.5/5和55%的研究被评为严谨性低;主要差距包括有限的真实数据使用和不一致的可靠性报告.
结论:
- 基于LLM的AI健康教练的评估目前是分散的,而且在方法上很弱.
- 需要多维验证框架,整合技术基准和以人为中心的方法.
- 确保人工智能教练的安全,有效和公平部署需要强有力的验证.
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