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With the increasing prevalence of dementia, timely access to assessments, diagnosis, and support is vital. However, the shortage of dementia specialists often leads to significant delays in a patient accessing time-sensitive treatments. To address this challenge, we proposed MemoryChat, a conversational dementia assessment tool designed to collect crucial information on the patient’s condition before the first clinical consultation. The Large Language Model-powered pipeline interacts with an informant to conduct a dynamic, multi-turn dialogue, collecting details. This interaction is transformed into a clinical summary and a preliminary diagnosis, serving as a reference for the clinician. Additionally, we introduce a novel clinical question dataset, \(\mathbf {MemoryChatQ_{Bank}}\) , which provides content to facilitate a comprehensive and standardised assessment of a person with suspected dementia. Our evaluation framework systematically assesses the clinical viability of MemoryChat by measuring the conversational, summarisation, and diagnostic capabilities. We compared different strong-performing Large Language Models, including Mistral:7b, LLaMA3.1:8b, and Qwen3:14b. We found that the configuration of LLaMA3.1-8b for interactivity and summarisation, alongside Qwen3-14b for diagnostic prediction, would formulate a potential option for MemoryChat to be used in a clinical setting.

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MemoryChat: Leveraging Large Language Models as Conversational Agents to Transform the Cognitive Assessment in Dementia Care

  • 会议论文

  • 发表日期：

    2026年

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  • Alex Robertson,
  • Huizhi Liang,
  • Judith Harrison

摘要

With the increasing prevalence of dementia, timely access to assessments, diagnosis, and support is vital. However, the shortage of dementia specialists often leads to significant delays in a patient accessing time-sensitive treatments. To address this challenge, we proposed MemoryChat, a conversational dementia assessment tool designed to collect crucial information on the patient’s condition before the first clinical consultation. The Large Language Model-powered pipeline interacts with an informant to conduct a dynamic, multi-turn dialogue, collecting details. This interaction is transformed into a clinical summary and a preliminary diagnosis, serving as a reference for the clinician. Additionally, we introduce a novel clinical question dataset, \(\mathbf {MemoryChatQ_{Bank}}\) , which provides content to facilitate a comprehensive and standardised assessment of a person with suspected dementia. Our evaluation framework systematically assesses the clinical viability of MemoryChat by measuring the conversational, summarisation, and diagnostic capabilities. We compared different strong-performing Large Language Models, including Mistral:7b, LLaMA3.1:8b, and Qwen3:14b. We found that the configuration of LLaMA3.1-8b for interactivity and summarisation, alongside Qwen3-14b for diagnostic prediction, would formulate a potential option for MemoryChat to be used in a clinical setting.