ArticleFrontiers in dementia2024
Introduction to Large Language Models (LLMs) for dementia care and research.
Article in Frontiers in dementia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformer-Based Deep Learning Approaches for Speech-Based Dementia Detection: A Systematic Review.IEEE journal of biomedical and health informatics · 2026Pooled it
- Review
- Article
- Integrated digit-in-noise testing for hearing and cognitive screening.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Governing Generative AI for Healthy Ageing: A Normative Conceptual Framework for Societal Alignment, Epistemic Authority, and Value Convergence in Geriatric Care.Healthcare (Basel, Switzerland) · 2026Article
- Artificial intelligence in nursing practice for older adults with dementia: A narrative review informed by bibliometric mapping and implications for nurse-led research.Journal of Korean gerontological nursing · 2026Article
- Article
- Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.Seminars in neurology · 2026Review
- An online survey of encouragement strategies for older adults' daily worries: a conversational robot versus a human partner.Frontiers in robotics and AI · 2026Article
- A Generative AI Framework for Cognitive Intervention in Older Adults: An Integrated Engineering Design and Clinical Protocol.Healthcare (Basel, Switzerland) · 2025Article
- AI-enabled resilience modeling for brain health.Frontiers in molecular medicine · 2025Review
- Utility of artificial intelligence-based conversation voice analysis for detecting cognitive decline.PloS one · 2025Article
- Can Large Language Models Aid Caregivers of Pediatric Cancer Patients in Information Seeking? A Cross-Sectional Investigation.Cancer medicine · 2025Article
- Voice Assistants to Deliver Cognitive Stimulation Therapy for Persons Living with Dementia.Extended abstracts on Human factors in computing systems. CHI ConferenceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Dementia is a progressive neurodegenerative disorder that affects cognitive abilities including memory, reasoning, and communication skills, leading to gradual decline in daily activities and social engagement. In light of the recent advent of Large Language Models (LLMs) such as ChatGPT, this paper aims to thoroughly analyse their potential applications and usefulness in dementia care and research. Method: To this end, we offer an introduction into LLMs, outlining the key features, capabilities, limitations, potential risks, and practical considerations for deployment as easy-to-use software (e.g., smartphone apps). We then explore various domains related to dementia, identifying opportunities for LLMs to enhance understanding, diagnostics, and treatment, with a broader emphasis on improving patient care. For each domain, the specific contributions of LLMs are examined, such as their ability to engage users in meaningful conversations, deliver personalized support, and offer cognitive enrichment. Potential benefits encompass improved social interaction, enhanced cognitive functioning, increased emotional well-being, and reduced caregiver burden. The deployment of LLMs in caregiving frameworks also raises a number of concerns and considerations. These include privacy and safety concerns, the need for empirical validation, user-centered design, adaptation to the user's unique needs, and the integration of multimodal inputs to create more immersive and personalized experiences. Additionally, ethical guidelines and privacy protocols must be established to ensure responsible and ethical deployment of LLMs. Results: We report the results on a questionnaire filled in by people with dementia (PwD) and their supporters wherein we surveyed the usefulness of different application scenarios of LLMs as well as the features that LLM-powered apps should have. Both PwD and supporters were largely positive regarding the prospect of LLMs in care, although concerns were raised regarding bias, data privacy and transparency. Discussion: Overall, this review corroborates the promising utilization of LLMs to positively impact dementia care by boosting cognitive abilities, enriching social interaction, and supporting caregivers. The findings underscore the importance of further research and development in this field to fully harness the benefits of LLMs and maximize their potential for improving the lives of individuals living with dementia.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.