---
title: "Caregivers’ views on AI-enabled dementia care: protocol for a qualitative evidence synthesis"
id: "plos-one-2-family-and-informal-caregivers-acceptance-concerns-and-expectations-regarding"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-2-family-and-informal-caregivers-acceptance-concerns-and-expectations-regarding"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163"
published_at: "2026-10-07T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Caregivers’ views on AI-enabled dementia care: protocol for a qualitative evidence synthesis
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-2-family-and-informal-caregivers-acceptance-concerns-and-expectations-regarding
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163)
- **Published At:** 2026-10-07T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This protocol outlines a qualitative evidence synthesis to examine family and informal caregivers’ **acceptance**, **concerns** and **expectations** about **AI-enabled dementia care**. - The review follows Joanna Briggs Institute (JBI) qualitative review methodology and is reported using PRISMA-P and ENTREQ guidance. - Searches will cover PubMed, Embase, CINAHL, PsycINFO and Web of Science from inception through January 2027, with the focused search window conducted Oct–Dec 2026. - Eligible studies are peer-reviewed qualitative or mixed-methods papers in English reporting caregivers’ perspectives on AI technologies used in dementia care; non-AI digital interventions and studies without caregiver data are excluded. - The SPIDER framework defines sample (family/informal caregivers), phenomenon (AI-enabled technologies), design (qualitative methods), evaluation (attitudes, perceptions, trust, concerns, expectations) and research type (qualitative/mixed-methods). - Study selection and quality appraisal will be performed independently by two reviewers using the JBI Critical Appraisal Checklist for Qualitative Research; disagreements resolved by discussion or a third reviewer. - Data will be extracted with a predefined form and synthesized using Thomas and Harden’s thematic synthesis approach; confidence assessed with GRADE-CERQual. - Timeline: searches Oct–Dec 2026; screening to January 2027; extraction by March 2027; synthesis by May 2027; final review by July 2027; protocol registered on PROSPERO (CRD420261470957). - Ethical approval is not required; findings will be published in a peer-reviewed journal. Funding and competing interests were declared absent by the authors. - The review aims to inform development, implementation and governance of caregiver-centered AI for dementia and to identify gaps for future research.
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosone%2Farticle%3Fid%3D10.1371%2Fjournal.pone.0360163) * * About * Browse * Publish * [](https://journals.plos.org/plosone/ "PLOS One") * Search [advanced search](https://journals.plos.org/plosone/search) * [Browse Topics](https://journals.plos.org/plosone/subjectAreaBrowse) Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click [here](https://github.com/PLOS/plos-thesaurus/blob/master/README.md "Link opens in new window"). [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163) [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163) * 0 [Save](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360163#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360163#savedHeader) * 0 [Citation](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360163#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360163#citedHeader) * 17 [View](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360163#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360163#viewedHeader) * 0 [Share](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360163#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360163#discussedHeader) Open Access Study Protocol # Family and informal caregivers’ acceptance, concerns and expectations regarding artificial intelligence-enabled dementia care: a protocol for qualitative evidence synthesis * Fang Wang , Contributed equally to this work with: Fang Wang, Jianqiong Li Roles Methodology, Writing – original draft Affiliation School of Nursing, Chongqing Three Gorges Medical College, Chongqing, China ⨯ * Jianqiong Li , Contributed equally to this work with: Fang Wang, Jianqiong Li Roles Methodology, Writing – original draft Affiliation School of Nursing, Chongqing Three Gorges Medical College, Chongqing, China ⨯ * Yingjie Shen Roles Conceptualization, Project administration, Writing – review & editing * E-mail: shenyingjie2024@163.com Affiliation Department of Nursing Science, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-3637-8449 ](https://orcid.org/0000-0002-3637-8449 "ORCID Registry") ⨯ # Family and informal caregivers’ acceptance, concerns and expectations regarding artificial intelligence-enabled dementia care: a protocol for qualitative evidence synthesis * Fang Wang, * Jianqiong Li, * Yingjie Shen ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: October 7, 2026 * * [Article](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163) * [Authors](https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0360163) * [Metrics](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360163) * [Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0360163) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pone.0360163) * [Abstract](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#abstract0) * [Introduction](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#sec005) * [Methods](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#sec006) * [Discussion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#sec021) * [Supporting information](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#sec022) * [References](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#references) * [Reader Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0360163) * [Figures](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163) ## Abstract ### Introduction Artificial intelligence (AI) is increasingly being integrated into dementia care to support assessment, monitoring, decision-making and personalized care. However, the successful implementation of AI-enabled dementia care depends not only on technological capabilities but also on the acceptance, trust and engagement of family and informal caregivers. Existing qualitative evidence remains fragmented and has not comprehensively synthesized caregivers’ perspectives regarding AI-enabled dementia care. This qualitative evidence synthesis aims to address this gap by exploring caregivers’ acceptance, concerns and expectations. ### Methods and analysis This qualitative evidence synthesis will be conducted in accordance with the Joanna Briggs Institute (JBI) methodology for qualitative systematic reviews and reported following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) statement. Studies will be identified through systematic searches of PubMed, Embase, CINAHL, PsycINFO and Web of Science from database inception to December 2026. Qualitative and mixed-methods studies exploring family and informal caregivers’ perspectives, experiences, acceptance, concerns or expectations regarding AI-enabled dementia care will be included. Study selection and methodological quality appraisal using the JBI Critical Appraisal Checklist for Qualitative Research will be conducted independently by two reviewers. Data will be extracted using a predefined form and synthesized using Thomas and Harden’s thematic synthesis approach. Confidence in the review findings will be assessed using the Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) approach. ### Ethics and dissemination Ethical approval is not required. Findings will be disseminated through peer-reviewed publication. ### Trial registration PROSPERO registration number: [CRD420261470957](https://clinicaltrials.gov/ct2/show/CRD420261470957). ## Figures ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360163.g001) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360163.g001) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360163.g001) **Citation:** Wang F, Li J, Shen Y (2026) Family and informal caregivers’ acceptance, concerns and expectations regarding artificial intelligence-enabled dementia care: a protocol for qualitative evidence synthesis. PLoS One 21(10): e0360163. https://doi.org/10.1371/journal.pone.0360163 **Editor:** Satabdi Mitra, KPC Medical College and Hospital, INDIA **Received:** August 14, 2026; **Accepted:** September 17, 2026; **Published:** October 7, 2026 **Copyright:** © 2026 Wang et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. **Data Availability:** No datasets were generated or analysed during the current study. All relevant data from this study will be made available upon study completion. **Funding:** The author(s) received no specific funding for this work. **Competing interests:** The authors have declared that no competing interests exist. ## Introduction Dementia is a progressive neurodegenerative condition that affects more than 58 million people worldwide and is a leading cause of disability, dependency and long-term care needs among older adults [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref001)]. As populations age, the growing prevalence of dementia is placing substantial pressure on healthcare systems, social care services and families, highlighting the urgent need for innovative approaches to support high-quality, person-centered dementia care [[2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref002)]. In recent years, artificial intelligence (AI) has emerged as a promising technology with the potential to transform dementia care across the disease trajectory [[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref003)]. AI-enabled technologies, including machine learning algorithms, clinical decision support systems, conversational AI, intelligent monitoring systems and socially assistive robots, are increasingly being integrated into dementia care to support early detection, personalized care planning, symptom monitoring, risk prediction and daily care management [[4](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref004)–[6](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref006)]. These technologies have the potential to enhance care efficiency, facilitate timely decision-making, promote independent living and improve care coordination across home, community and healthcare settings [[7](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref007),[8](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref008)]. As AI applications continue to expand beyond diagnosis into the delivery and management of dementia care, understanding the factors that influence their successful implementation in real-world care settings has become an increasingly important priority for researchers, clinicians, technology developers and policymakers [[9](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref009)]. Family and informal caregivers are central to the delivery of dementia care due to the inaccessibility of costly professional care, lack of insurance support, and cultural barriers [[10](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref010)]. As dementia progresses and cognitive impairment worsens, caregivers frequently assume increasing responsibility for monitoring health status, managing behavioral and psychological symptoms, communicating with healthcare professionals and making care-related decisions on behalf of the person living with dementia [[11](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref011)]. Consequently, they are among the primary end-users and decision-makers regarding the adoption and sustained use of AI-enabled technologies in dementia care. Although AI has the potential to enhance caregiving by improving care coordination, reducing caregiving burden and supporting timely decision-making, its successful implementation depends not only on technological capability but also on caregivers’ willingness to accept, trust and integrate these technologies into routine care [[12](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref012)]. Family and informal caregivers may simultaneously perceive AI as a source of practical support while expressing concerns regarding privacy, data security, ethical implications, reduced human interaction, accountability and the potential replacement of person-centered care [[13](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref013),[14](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref014)]. They may also hold diverse expectations regarding how AI should complement, rather than replace, human caregiving [[15](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref015)]. Therefore, understanding caregivers’ acceptance, concerns and expectations is essential for developing, implementing and governing AI-enabled dementia care that is clinically meaningful, ethically responsible and responsive to the needs and preferences of families. A growing body of qualitative research has explored caregivers’ experiences with digital technologies designed to support dementia care [[16](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref016)]. Existing evidence syntheses have examined specific interventions, such as socially assistive robots and supportive mobile applications, providing valuable insights into caregivers’ experiences, perceived benefits and implementation challenges associated with individual technologies [[15](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref015),[17](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref017)]. However, these reviews have largely focused on single technological modalities rather than the broader and rapidly evolving landscape of artificial intelligence-enabled dementia care [[18](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref018),[19](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref019)]. AI-enabled technologies differ fundamentally from conventional digital interventions because they increasingly incorporate autonomous decision-making, predictive analytic, adaptive learning and data-driven personalization, giving rise to distinctive practical, ethical and governance considerations [[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref005),[20](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref020),[21](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref021)]. Consequently, findings derived from reviews of individual technologies cannot fully explain how family and informal caregivers understand, evaluate and respond to AI-enabled dementia care across different applications and care settings. Moreover, while an increasing number of qualitative studies have explored caregivers’ views on AI-supported dementia care, the evidence remains fragmented across diverse technologies, contexts and study populations [[12](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref012),[17](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref017)]. To date, few qualitative evidence has comprehensively integrated family and informal caregivers’ acceptance, concerns and expectations regarding AI-enabled dementia care. As a result, there remains limited understanding of the factors influencing caregivers’ acceptance of AI, the concerns that may hinder its implementation, and the expectations that should inform the development of safe, trustworthy and caregiver-centered AI solutions for dementia care. A qualitative evidence synthesis is well suited to addressing this evidence gap because it enables the systematic integration and interpretation of caregivers’ lived experiences, perspectives and contextual understandings across diverse qualitative studies [[22](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref022)]. By synthesizing qualitative evidence, this review will move beyond describing individual study findings to generate a more comprehensive understanding of the factors shaping family and informal caregivers’ acceptance, concerns and expectations regarding AI-enabled dementia care. Such evidence is essential for informing the development of caregiver-centered AI technologies that are not only technically effective but also acceptable, trustworthy and ethically responsible in real-world care settings. The findings of this review may also support healthcare professionals, technology developers and policymakers in designing, implementing and governing AI-enabled dementia care that better aligns with the needs, values and preferences of families caring for people living with dementia. In addition, the review will identify gaps in the current qualitative evidence base and provide directions for future research on the responsible integration of AI into dementia care. Therefore, this qualitative evidence synthesis aims to systematically synthesize qualitative evidence on family and informal caregivers’ acceptance, concerns and expectations regarding artificial intelligence-enabled dementia care, thereby informing the future development, implementation and governance of caregiver-centered AI in dementia care. ## Methods ### Study design and protocol This protocol describes a qualitative evidence synthesis that aims to systematically synthesize qualitative evidence on family and informal caregivers’ acceptance, concerns and expectations regarding artificial intelligence (AI)-enabled dementia care. A qualitative evidence synthesis was selected because it enables the integration and interpretation of findings from qualitative studies to generate a comprehensive understanding of participants’ perspectives, meanings and contextual experiences across diverse settings [[23](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref023)]. The review will be conducted in accordance with the Joanna Briggs Institute (JBI) methodology for qualitative systematic reviews, which provides a rigorous and transparent approach for identifying, appraising, synthesizing and reporting qualitative evidence [[24](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref024)]. The protocol has been developed and reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 Statement [[25](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref025)]. The completed review will be reported in accordance with the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) statement to ensure comprehensive and transparent reporting of the review process and findings [[26](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360163#pone.0360163.ref026)]. This review protocol has been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (registration number: CRD420261470957). Any important amendments to the protocol made during the review process will be documented, justified and reported in the final publication to ensure transparency and reproducibility. ### Study status and timeline This manuscript presents a protocol for a qualitative evidence synthesis. At the time of submission, the review has not yet commenced. The systematic literature search is planned to be conducted in October 2026. Record screening is expected to be completed by January 2027, and data extraction is expected to be completed by March 2027. Data synthesis and interpretation are expected to be completed by May 2027, with the final review results anticipated by July 2027. These dates represent the anticipated timeline and may be adjusted depending on the volume of retrieved records, the number of eligible studies, and the progress of the review. ### Eligibility criteria Eligi
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