Evidence map›Paper›PMID 42051326›Full record

ArticleFrontiers in digital health2026

Co-designing animated videos to explain large language models and their use in healthcare and research.

Elizabeth Remfry, Jaya Chaturvedi, Sarah Markham, Elizabeth Ford, Mel Ramasawmy

Abstract read
In one paragraph

Article in Frontiers in digital health, 2026. 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
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0citing papers in PubMed
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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

5 authors.

Elizabeth Remfry *Queen Mary University of London, London, United Kingdom.
Jaya Chaturvedi *King's College London, London, United Kingdom.
Sarah MarkhamKing's College London, London, United Kingdom.
Elizabeth FordBrighton and Sussex Medical School, Brighton, United Kingdom.
Mel Ramasawmy *Queen Mary University of London, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The increasing development of large language models (LLM) in healthcare research is taking place without patient and public involvement and engagement (PPIE). Part of the challenge is the lack of accessible educational resources to promote literacy around LLMs. Methods: We employed a co-design approach with 6 PPIE contributors from Tower Hamlets, London to develop educational animations about LLMs. We conducted 7 pilot sessions that included hands-on interactive activities to develop scripts and storyboards for the animations. Animations were external validated by additional PPIE groups and experts before professional animation production. Results: Two accessible 2-minute animated videos were successfully co-designed in English and Bengali. The first explains LLMs in the context of daily life and the second on LLMs in healthcare research. Both animations use a factory analogy to describe LLMs functionality while avoiding anthropomorphisation that could mislead audiences about LLMs inner workings and capabilities. Both animations are freely available for reuse. Discussion: The co-design process allowed us to prioritise diverse PPIE voices throughout development, ensuring that animations are appropriate for a wide audience. Challenges included incorporating sometimes conflicting perspectives and achieving balanced portrayals of LLM benefits and risks. We hope these animations will support PPIE contributors to have a say in how LLMs are used in healthcare and research.

Indexed as

AI literacyco-designlarge language modelsLLMspatient and public involvement and engagementPPIE

Identifiers

PMID42051326
PMCPMC13111438

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.