The landscape of knowledge graph and large language model-augmented knowledge graph applications in dementia caregiving support: a scoping review
Abstract
BACKGROUND AND OBJECTIVES: Dementia's rising prevalence places an immense burden on caregivers. Knowledge graphs (KGs) and large language model (LLM)-augmented KGs are emerging Artificial Intelligence (AI) approaches that organize complex dementia care knowledge and enable personalized, context-aware support, yet this field remains nascent. We aimed to map and synthesize research on KGs and LLM-augmented KGs in dementia caregiving, identifying system types, applications, outcomes, challenges, and ethical considerations.
RESEARCH DESIGN AND METHODS: Following the Joanna Briggs Institute (JBI) framework, a comprehensive search was conducted across 6 academic databases (PubMed, Scopus, Web of Science, IEEE Xplore, PsycINFO, CINAHL) and gray literature. Eligibility criteria included studies detailing the design, development, or evaluation of KGs or LLM-augmented KGs for dementia caregiving.
RESULTS: 12 articles representing 11 unique studies met the inclusion criteria. All 11 studies used KG or ontology components; 8 were KG-only systems, often supporting personalized meal planning, care plan recommendations, knowledge management, robotic assistance, or virtual assistants. 3 studies described LLM-augmented KGs (3/11), primarily using retrieval-augmented generation to enhance conversational AI for caregivers or persons with dementia. Reported benefits included improved usability, personalized support, more accurate or relevant recommendations, and potential improvements in…
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