认知查中的代理人工智能:痴呆症护理的翻译路线图
Kevin Mekulu1, Faisal Aqlan2, Hui Yang1
1Complex Systems Monitoring, Modeling and Control Laboratory, Pennsylvania State University, University Park, PA, USA.
Journal of Alzheimer's disease reports
|December 15, 2025
概括
代理人工智能 (AI) 系统提供适应性,可解释性痴呆症查. 这些模块化大型语言模型 (LLM) 代理反映了针对个性化认知健康洞察的跨学科护理.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 医疗信息学 医疗信息学
背景情况:
- 目前的痴呆症查工具 (基于纸张的,静态的AI) 提供了有限的,定期的认知功能快照.
- 认知衰退是动态的,受到环境因素,并发病症和患者病史的影响.
- 现有的方法缺乏适应性,反集成和透明的推理,这对于临床信任至关重要.
研究的目的:
- 提出代理人工智能系统作为动态和个性化的认知健康评估的新框架.
- 介绍认知代理实验室作为这种代理人工智能框架的实际例子.
- 突出用于临床应用的代理AI的关键功能和非功能方面.
主要方法:
- 开发基于大型语言模型 (LLM) 构建的模块化代理.
- 强调功能方面:输入,输出和协作工作流.
- 专注于非功能方面:透明度,适应性,稳定性和临床调整.
主要成果:
- 代理人工智能系统展示了协作和适应性认知评估的潜力.
- 认知代理实验室框架为实施这些系统提供了一个模型.
- 拟议的方法整合了推理和协作,以提高临床效用.
结论:
- 代理人工智能系统代表了与静态选工具相比的重大进步.
- 这个框架为更个性化,可解释和可信赖的认知健康解决方案提供了一条途径.
- 整合LLM和模块化剂可促进动态和临床一致的认知监测.
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