Evidence map›Paper›PMID 40830991›Full record

ArticleResearch involvement and engagement2025

Targeting everyday decision makers in research: early career researcher and patient and public involvement and engagement collaboration in an AI-in-healthcare project.

Duncan J Reynolds, Susan Mountain, Victoria Bartle, Elizabeth Remfry, Michael R Barnes, Nick J Reynolds, Alexandra Thompson, AI MULTIPLY

Abstract read
In one paragraph

Article in Research involvement and engagement, 2025. 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.

Duncan J ReynoldsWolfson Institute of Population Health, Mary University of London, London, Queen, UK. Duncan.reynolds@qmul.ac.uk.
Susan MountainPatient and Public Involvement and Engagement Group, AI MULTIPLY, Newcastle, UK.
Victoria BartlePatient and Public Involvement and Engagement Group, AI MULTIPLY, Newcastle, UK.
Elizabeth RemfryWilliam Harvey Research Institute, Queen Mary University of London, London, UK.
Michael R BarnesWilliam Harvey Research Institute, Queen Mary University of London, London, UK.
Nick J ReynoldsNIHR Biomedical Research Centre and Department of Dermatology, Royal Victoria Infirmary, Newcastle Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK.
Alexandra ThompsonPopulation Health Sciences Institute Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK.
AI MULTIPLY

Funding

National Institute for Health and Care Research NIHR203982
6 · The paper itself

Abstract

Patient and Public Involvement and Engagement (PPIE) is critical in the development and application of Artificial Intelligence (AI) in healthcare research to ensure that outcomes align with patients' and the public's needs. However, current PPIE practices often limit involvement to reactive tasks such as reviewing documents and providing plain English summaries. Whilst important, this approach can sideline PPIE from influencing key research decisions. Consequently, PPIE interactions often fail to adequately reach and influence everyday decision makers. On AI and big data research projects, these decisions are often made by Early Career Researchers (ECRs) who play a vital role in the day-to-day research process. After realising these limitations, and to address them, the NIHR-funded AI MULTIPLY consortium introduced twice-monthly "ECRs meet PPIE" sessions. These sessions began in May 2024 and enabled ECRs to present and discuss work in progress and gain targeted input from PPIE members during early phases of research, such as research direction, data and variable selection. By integrating PPIE at this stage, the project aimed to improve the relevance and impact of the healthcare research but also provide ECRs with essential skills in public engagement. At time of writing, 12 sessions have been conducted. Through ethnographic observations integrated with internal surveys, the findings show how the sessions were developed, overcame challenges, and helped to embed PPIE contributors' voices into an AI-in-healthcare project. Based on our findings we have identified 5 recommendations for other large interdisciplinary consortia to strengthen the contribution of PPIE to everyday decision-making in research.

Indexed as

Artificial intelligenceEarly career researchersEthnographyHealthcare researchPatient and public involvement

Identifiers

PMID40830991
PMCPMC12366416

What OpenQuestion holds

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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.