Evidence map›Paper›PMID 39956979›Full record

ArticleJournal of Alzheimer's disease : JAD2025

Cultural variation in trust and acceptability of artificial intelligence diagnostics for dementia.

Avinash Chandra, Kaviya Senthilvel, Rifah Anjum, Ijeoma Uchegbu, Laura J Smith, Helen Beaumont, Reshma Punjabi, Samina Begum, Charles R Marshall

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of Alzheimer's disease : JAD, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Avinash ChandraCentre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.ORCID 0009-0005-5072-7499
Kaviya SenthilvelSchool of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.
Rifah AnjumCentre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
Ijeoma UchegbuCentre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.ORCID 0000-0003-2952-8944
Laura J SmithCentre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.ORCID 0000-0003-3275-1530
Helen BeaumontAINOSTICS LTD, 3 Hardman Square, Spinningfields, Manchester, UK.ORCID 0000-0002-8446-3939
Reshma PunjabiABATED Study Lay Steering Committee Member, UK.
Samina BegumABATED Study Lay Steering Committee Member, UK.
Charles R MarshallCentre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.

Funding

Department of Health NIHR203373
6 · The paper itself

Abstract

Digital health innovations hold diagnostic and therapeutic promise but may be subject to biases for underrepresented groups. We explored perceptions of using artificial intelligence (AI) diagnostics for dementia through a focus group as part of the Automated Brain Image Analysis for Timely and Equitable Dementia Diagnosis (ABATED) study. Qualitative feedback from a diverse public engagement group indicated that cultural variations in trust and acceptability of AI diagnostics may be an unrecognised source of real-world inequity. Efforts focused on the adoption of AI diagnostics in memory clinic pathways should aim to recognise and account for this issue.

Indexed as

Artificial IntelligenceDementiaPatient Acceptance of Health CareTrustAgedFemaleFocus GroupsHumansMaleMiddle AgedacceptabilityAlzheimer's diseaseartificial intelligencedementia diagnosisdiagnosticshealth inequalitiesmachine learningtrust

Identifiers

PMID39956979
PMCPMC7617421

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.