The search for early biomarkers of mild cognitive impairment (MCI) has been central to the Alzheimer’s Disease (AD) and dementia research community in recent years. To identify MCI status at the earliest possible point, recent studies have shown that linguistic markers such as word choice, utterance and sentence structures can potentially serve as preclinical behavioral markers. Here we present an adaptive dialogue algorithm (an AI-enabled dialogue agent) to identify sequences of questions (a dialogue policy) that distinguish MCI from normal (NL) cognitive status. Our AI agent adapts its questioning strategy based on the user’s previous responses to reach an individualized conversational strategy per user. Because the AI agent is adaptive and scales favorably with additional data, our method provides a potential avenue for large-scale preclinical screening of neurocognitive decline as a new digital biomarker, as well as longitudinal tracking of aging patterns in the outpatient setting.

近年来，寻找轻度认知障碍（MCI）的早期生物标志物一直是阿尔茨海默氏病（AD）和痴呆研究界的核心。为了尽早识别MCI状态，最近的研究表明，诸如单词选择，话语和句子结构之类的语言标记可以潜在地充当临床前行为标记。在这里，我们提出一种自适应对话算法（一种支持AI的对话代理），以识别将MCI与正常（NL）认知状态区分开的问题序列（对话策略）。我们的AI代理会根据用户的先前响应来调整其提问策略，以实现每个用户的个性化会话策略。由于AI代理具有自适应性，并且可以通过附加数据很好地扩展，