arXiv:2605.08480cs.AI2026-05

用对话式AI帮阿尔茨海默病患者管理日常事务,降低使用门槛。

AI-Care: A Conversational Agentic System for Task Coordination in Alzheimer's Disease Care

  • 基于LangGraph的对话状态管理,支持多轮自然语言交互
  • 通过护理人员验证的记录确保用药等关键信息安全可靠
  • 适合认知障碍患者使用的语音优先系统,支持清晰语音输出

阿尔茨海默病(AD)及相关痴呆症(ADRD)患者因记忆力和思维能力下降,难以独立使用数字日程管理工具。本文提出AI-Care,一个建立在远程照护平台之上的对话式智能代理系统,专为AD/ADRD人群设计。该系统通过语音优先的聊天机器人,支持自然语言交互,帮助用户完成设置日历提醒、整理待办事项等日常任务,减轻认知负担。系统采用基于LangGraph的状态化编排,请求依次经过清洗、意图识别、上下文加载、安全检查、确定性槽位收集、工具执行和响应生成。涉及药物和过敏等安全关键内容时,均基于护理人员验证的数据,而非模型自由生成。系统不进行自主医疗决策。对于不完整或模糊请求,通过受控多轮澄清处理,避免沉默失败或猜测。支持文本与语音输入,语音输出由ElevenLabs合成,长回复分段处理以避免播放过快。初步试点对四位轻度至中度AD/ADRD患者进行评估,结果显示用户认为系统可信、专业且易于接受,能够通过对话完成指定协调任务。本文阐述了设计目标、系统架构、安全机制及初步评估发现。

原文摘要 · Abstract (English)

Individuals with Alzheimer's disease (AD) and Alzheimer's disease-related dementia (ADRD) experience memory and thinking changes that impact their ability to use digital daily management tools. For example, adding an event to a digital calendar requires multiple steps that may act as barriers to independent use for individuals with AD/ADRD. This paper presents AI-Care, a conversational agentic artificial intelligence (AI) layer built on top of a remote caregiving platform co-designed with people with AD/ADRD. AI-Care is designed to reduce the cognitive load on individuals with AD/ADRD when managing everyday tasks such as setting calendar reminders and organizing to-do lists through natural-language interaction with a voice-first chatbot. The system uses a LangGraph-based stateful orchestration approach in which each request passes through sanitization, intent classification, context loading, safety checks, deterministic slot collection, tool execution, and response composition. Safety-critical responses, particularly around medications and allergies, are grounded in caregiver-verified records rather than free-form model generation. The system does not make autonomous medical or treatment decisions. Incomplete or ambiguous requests are handled through controlled multi-turn clarification rather than silent failure or guessing. The system supports both typed and spoken input, with voice output through ElevenLabs text-to-speech. Longer responses are chunked before synthesis to avoid rushed playback. A preliminary pilot with four individuals with mild-to-moderate AD/ADRD showed that users found the system trustworthy, competent, and likable, and were able to complete the evaluated coordination tasks through conversation. We describe the design goals, system architecture, safety controls, and findings from this formative evaluation.

对话系统认知辅助老年健康

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