MemoryChat: Leveraging Large Language Models as Conversational Agents to Transform the Cognitive Assessment in Dementia Care
Year: 2025
Type: conference-paper
Abstract: 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, MemoryChatQ, 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.
DOI: 10.57711/mham-6e86
Author: Robertson A, Liang H, Harrison J
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7086757552
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