Evidence map›Paper›PMID 42852573›Full record

ArticleJournal of medical Internet research2026

Do Androids Dream of Lived Experience? Navigating Risks, Uncertainty, and Best Practices Around Generative AI in Patient and Public Involvement and Engagement.

William Lammons, Selina Wallis, Yuncong Liu, Lorraine Cezair-Phillip

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of medical Internet research, 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
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

4 authors.

William LammonsPrimary Care and Population Health, University College London, 1-19 Torrington Place, London, England, WC1E 7HB, United Kingdom, 44 20 3108 5599.ORCID http://orcid.org/0000-0002-8302-9831
Selina WallisNIHR ARC North West Coast, Liverpool, England, United Kingdom.ORCID http://orcid.org/0000-0002-5490-0528
Yuncong LiuPrimary Care and Population Health, University College London, 1-19 Torrington Place, London, England, WC1E 7HB, United Kingdom, 44 20 3108 5599.ORCID http://orcid.org/0000-0002-9023-1384
Lorraine Cezair-PhillipNIHR ARC North Thames, London, England, United Kingdom.ORCID http://orcid.org/0009-0008-3727-8344

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: In this viewpoint paper, we, as professionals working in patient and public involvement and engagement (PPIE), argue that the use of generative AI poses risks to the place of lived experience in PPIE because of its ability to generate outputs that can potentially eclipse those of people with lived experience. Generative AI appears to offer solutions to the National Health Service (NHS) and health research in an era of change and uncertainty-overstretched health infrastructure, unclear leadership, burned-out staff, and cuts and redundancies. NHS leaders are also pursuing the integration of generative AI into the NHS, in part to realize its "analogue to digital" plans. But those solutions come with commensurate risks. In this context, we have authored this viewpoint, aiming to (1) explain the risks that generative AI poses to those working with lived experience in PPIE contexts (distorted and biased outputs, contributing to inequalities, minimizing or replacing patient voices, and limiting learning); (2) clarify the positive opportunities that generative AI offers to PPIE (speeding up diagnostics, accessibility accommodations); and (3) provide a checklist that gives professionals clear pragmatic advice on using generative AI ethically in PPIE.

Indexed as

Artificial IntelligenceCommunity ParticipationPatient ParticipationGenerative Artificial IntelligenceHumansUncertaintyAI chatbotsartificial intelligencelived experiencepatient and public involvement and engagementPPIE

Identifiers

PMID42852573

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.