用对话模型辅助阿尔茨海默病早期诊断，提升症状捕捉能力。
Designing and Evaluating a Conversational Agent for Early Diagnosis of Alzheimer's Disease and Related Dementias
- 基于大语言模型设计语音对话代理，主动收集患者叙事
- 30人测试中，症状识别与专家访谈结果高度一致
- 适合临床辅助诊断，尤其擅长引导复杂体验表达
阿尔茨海默病及相关痴呆（ADRD）的早期诊断对及时干预至关重要，但多数病例仍延迟至晚期才确诊。尽管完整患者叙事对准确诊断至关重要，现有研究多聚焦于从交互中分类认知状态，而非支持诊断过程。本文设计了基于大语言模型（LLMs）的语音互动对话代理，用于从患者及照护者处获取与ADRD相关的叙事内容。通过30名疑似ADRD成人参与者，结合对话分析、用户问卷及与盲法专家访谈的症状对比，评估该代理的表现。结果显示，代理所发现的症状与专家识别结果具有良好一致性。用户普遍认可其耐心和系统性提问，有助于提升参与度并表达难以描述的复杂体验。这些发现表明，对话代理在作为结构化诊断辅助工具方面具备潜力，但需更大样本验证及临床实用性评估后方可部署。
原文摘要 · Abstract (English)
Early diagnosis of Alzheimer's disease and related dementias (ADRD) is critical for timely intervention, yet most diagnoses are delayed until advanced stages. While comprehensive patient narratives are essential for accurate diagnosis, prior work has largely focused on screening studies that classify cognitive status from interactions rather than supporting the diagnostic process. We designed voice-interactive conversational agents, leveraging large language models (LLMs), to elicit narratives relevant to ADRD from patients and informants. We evaluated the agent with 30 adults with suspected ADRD through conversation analysis, user surveys, and analysis of symptom elicitation compared to blinded specialist interviews. Symptoms detected by the agent showed promising agreement with those identified by specialists. Users appreciated the agent's patience and systematic questioning, which supported engagement and expression of complex, hard-to-describe experiences. While these findings suggest potential for conversational agents as structured diagnostic support tools, further validation with larger samples and assessment of clinical utility is needed before deployment.
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