Evidence map›Paper›PMID 41766171›Full record

ArticleHealth expectations : an international journal of public participation in health care and health policy2026

Using Arts-Based Methods to Involve People Living in Tower Hamlets With Multiple Long-Term Conditions in the Development of Artificial Intelligence Tools in Healthcare Research.

Elizabeth Remfry, Duncan J Reynolds, Sylvia Morgado de Queiroz, Social Action For Health, Rohini Mathur, Michael R Barnes, Alison Thomson, AI‐Multiply PPIE Group and AI‐Multiply

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Article in Health expectations : an international journal of public participation in health care and health policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Elizabeth RemfryWilliam Harvey Research Institute, Queen Mary University of London, London, UK.ORCID 0000-0003-3940-8540
Duncan J ReynoldsWolfson Institute of Population Health, Queen Mary University of London, London, UK.ORCID 0000-0001-7580-4917
Sylvia Morgado de QueirozOxford Brookes University, Oxford, UK.
Social Action For HealthSocial Action for Health, Brady Arts Centre, London, UK.
Rohini MathurWolfson Institute of Population Health, Queen Mary University of London, London, UK.
Michael R BarnesWilliam Harvey Research Institute, Queen Mary University of London, London, UK.
Alison ThomsonWolfson Institute of Population Health, Queen Mary University of London, London, UK.ORCID 0000-0002-9299-0130
AI‐Multiply PPIE Group and AI‐MultiplyInstitute of Translational and Clinical Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.

Funding

National Institute for Health and Care Research NIHR203982Wellcome TrustWellcome Trust 218584/Z/19/Z
6 · The paper itself

Abstract

backgroundIncluding public contributors in the development of artificial intelligence (AI) systems in healthcare research is growing, however, traditional methods of participation fail to engage people from minoritised groups. This work explores how we can utilise art-based methods to involve the perspectives of those not previously included in AI development.

methodsWe collaborated with a East London-based organisation to involve people not previously included in research to contribute to a study on multiple long-term conditions (MLTCs) and polypharmacy. Patient and public involvement and engagement (PPIE) contributors all had lived experience of MLTCs and represented a range of different ages, genders, socio-demographic backgrounds and multilingual abilities. We ran a series of six workshops that used different visual arts methods; ceramics, collage, body mapping and AI-generated images, to create research priorities and to inform AI development.

findingsThe arts-based methods served as a platform for communication which supported PPIE contributors to develop multiple research priorities, for example the impact of the lack of routine appointments on MLTCs. Through these workshops PPIE contributors also highlighted concepts that are important to consider during AI model development, such as utilising local housing data and considering bias. Visual images and art helped to facilitate different forms of communication, whilst being fun and engaging and provided a way to make abstract AI concepts more tangible whilst building AI literacy.

conclusionsArts-based methods were a useful tool to make involvement in research more accessible for under-represented communities in the development of AI tools in healthcare research. There is a need for more inclusive participatory approaches as the use of AI in healthcare and research increases. PATIENT OR PUBLIC CONTRIBUTION: Working with staff and interpreters from a local community-based charity, Social Action for Health, we invited 22 PPIE contributors from under-represented communities in Tower Hamlets who had no previous experience of PPIE research. PPIE contributors developed the research priorities for a large academic consortia and helped create a community art exhibition to highlight their artwork. Additionally, two experienced PPIE contributors from the wider AI-Multiply study assisted with the preparation of this manuscript.

Indexed as

ArtArtificial IntelligenceCommunity ParticipationHealth Services ResearchChronic DiseaseFemaleHumansLondonartificial intelligencearts‐based methodsparticipatory researchPPIEpublic and patient involvement and engagement

Identifiers

PMID41766171
PMCPMC12950819

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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.