基于大型语言模型的身体活动和认知训练代理:范围审查.
Alessandro Silacci1,2, Benedetta Giachetti3,4, Leonardo Angelini2,3
1Department of Information Systems, Faculty of Business and Economics, University of Lausanne, Quartier Centre, Lausanne, 1015, Switzerland, 41 21 692 11 11.
JMIR AI
|March 12, 2026
概括
大型语言模型 (LLM) 代理对体力活动和认知训练干预有希望. 然而,目前的研究是有限的,强调需要对这些数字健康工具进行更严格和透明的评估.
科学领域:
- 数字健康数字健康
- 医疗保健中的人工智能
- 人与计算机的交互
背景情况:
- 基于大型语言模型 (LLM) 的对话代理越来越多地用于数字健康.
- 它们在身体活动 (PA) 和认知训练 (关键的福祉领域) 中的应用需要系统地绘制.
- 这些领域需要个性化,适应性支持和对话参与,使它们适合LLM代理部署.
研究的目的:
- 系统地绘制基于LLM的对话代理的范围,特征和设计实践,用于PA和认知训练.
- 分析这些代理的应用环境,社会角色和技术特征.
- 了解目前在这些福利领域的LLM应用的现状.
主要方法:
- 根据PRISMA-ScR指南进行范围审查.
- 从2018年1月到2024年12月,搜索了主要的学术数据库 (科学网络,Scopus,PubMed,ACM,IEEE Xplore).
- 包括描述PA或认知训练的LLM代理人的研究,使用描述性合成和框架分析.
主要成果:
- 10项研究符合资格标准 (7项PA,3项认知培训).
- 代理人主要担任PA的教练角色和认知训练的伴侣/脚手架角色.
- 专有LLM (GPT-3.5,GPT-4,Bard) 占主导地位;即时工程是关键,但报告不一致;结果侧重于感知到的有用性和参与度,而不是行为变化.
结论:
- LLM代理商对PA和认知培训支持有着早期的承诺,但证据是探索性的和有限的.
- 挑战包括不一致的快速报告,依赖专有模型和缺乏标准化的结果措施.
- 需要更严格,更透明的评估来加强证据,并指导未来基于LLM的数字健康发展.
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