| Sumario: | 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 quality of life and independence. Key challenges involved technical complexity, KG maintenance, data quality, limited real-world evaluation, and underdeveloped ethical analysis. Discussion and Implications Integrating KGs with LLMs for dementia caregiving is a promising yet nascent interdisciplinary field. While early systems demonstrate potential, significant gaps remain in clinical validation, comprehensive ethical guidelines development, and responses to caregivers' diverse and evolving needs.
|
|---|