{
  "abstract": "There are over five-hundred-thousand individuals with recorded dementia diagnoses in England, highlighting an urgent need for scalable and effective care solutions to ease pressure on health and social care systems. Artificial Intelligence (AI)-enabled smart home systems are emerging as promising digital health innovations, offering cognitive support, real-time monitoring, and decision-making assistance. However, concerns around trust, user agency, and misconceptions about AI continue to limit acceptance and may hinder adoption. This study examined how stage of dementia care, system design, and level of system involvement shape public attitudes towards AI-driven care technologies. A repeated-measures design was employed, using vignettes that varied across three dimensions: stage of care (aligned with NHS England’s Dementia Well Pathway), system centrism (AI-based versus human-based), and level of involvement (moderate support versus full control). Participants aged 55–64 years – a group at elevated risk of developing dementia or encountering these technologies within the next decade – rated each scenario on both acceptability and perceived likelihood of actualisation/plausibility. Findings showed that acceptability was sensitive to both care stage and system type. A significant interaction indicated that full human involvement was consistently rated as more acceptable, while AI involvement was viewed more favourably only at moderate levels. This interaction effect intensified across the dementia care pathway, with the largest discrepancies observed in later stages such as Living well and Dying well, where full-control AI was rated least acceptable. In terms of perceived likelihood, scenarios were judged more likely under conditions of moderate involvement, with human-centric scenarios rated as more likely than AI-centric ones, particularly in later stages. These results highlight the importance of trust, autonomy, and public understanding in AI adoption. Acceptance was highest when AI was positioned to augment rather than replace human input, supporting hybrid models that preserve agency while enhancing scalability. Author summary: Dementia diagnoses are increasing rapidly, creating an urgent need for innovative care solutions. Artificial Intelligence (AI) and AI-integrated smart-homes offer a promising way to provide cognitive care and support through remote monitoring, but public trust and acceptance remain a challenge. Here, we explored people’s affective attitudes towards AI for dementia care by presenting scenarios that varied based on stage of dementia (from preventative to end-of-life care), whether the system was AI or human-based, and the level of control the system had over the care (moderate or full). We surveyed a sample of older adults, a group of potential future users of this technology. Our findings show that people’s attitudes are highly dependent on factors such as stage of care, level of system control/involvement, and system centricity, human-centric systems were consistently rated as the most acceptable option, while AI was most favourable when offering moderate support rather than taking full control. This preference for human-centric, moderate support grew stronger in the later stages of dementia care. In summary, people find AI technology more acceptable when it is used to enhance human care, rather than replace it. This suggests that hybrid healthcare models, where AI is used to support and supplement caregivers, will be the most effective way to integrate these scalable technologies into dementia care.",
  "abstract_shown": true,
  "affiliations": [
    "Climate & Health Infodemic Resilience Programme (CHIRP), Northumbria University, Newcastle upon Tyne, United Kingdom",
    "Psychology and Communication Technology (PaCT) Lab, School of Psychology, Faculty of Health and Wellbeing, Northumbria University, Newcastle upon Tyne, United Kingdom",
    "School of Psychology, Faculty of Health and Wellbeing, Northumbria University, Newcastle upon Tyne, United Kingdom"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Thomas O’Fee"
    },
    {
      "affs": [
        1
      ],
      "name": "Santosh Vijaykumar"
    },
    {
      "affs": [
        2
      ],
      "name": "Michael Craig"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bx6fr2cm3y7b4.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pdig.0001088",
  "fulltext_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001088",
  "id": "bx6fr2cm3y7b4",
  "issns": [
    "2767-3170"
  ],
  "landing_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001088",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive neuroscience/Cognitive neurology/Cognitive impairment"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognition/Decision making"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive neuroscience/Cognitive neurology/Cognitive impairment"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Decision making"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Decision making"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Psychological attitudes"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Health care/Palliative care"
    ],
    [
      "plos",
      "/Medicine and health sciences/Mental health and psychiatry/Dementia"
    ],
    [
      "plos",
      "/Medicine and health sciences/Neurology/Cognitive neurology/Cognitive impairment"
    ],
    [
      "plos",
      "/Medicine and health sciences/Neurology/Dementia"
    ],
    [
      "plos",
      "/People and places/Population groupings/Age groups/Adults/Elderly"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Decision making"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Psychological attitudes"
    ]
  ],
  "openalex": "W7221061122",
  "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001088&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/bx6fr2cm3y7b4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pdig.0001088",
      "source": "plos",
      "url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001088"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:cognitive neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "medicine",
      "origin": "plos:neurology",
      "subfield": "neurology"
    }
  ],
  "title": "Examining public acceptance of AI versus human-centric dementia care across NHS England’s dementia pathway stages",
  "title_text": "Examining public acceptance of AI versus human-centric dementia care across NHS England’s dementia pathway stages",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/bx6fr2cm3y7b4",
  "venue": "PLOS Digital Health",
  "versions": []
}