Evidence map›Paper›PMID 42024494›Full record

ArticleEuropean stroke journal2026

Information leaflets vs artificial intelligence: comparing perceptions of stroke survivors and professionals in a mixed-methods study.

Lucie Tvrda, Jennifer K Burton, Katie McConnell, Kalliopi Mavromati, Hendrik Knoche, Robert Mikulik, Terence J Quinn

Abstract readComparative Study
In one paragraph

Article in European stroke journal, 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

7 authors.

Lucie TvrdaSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom.ORCID 0000-0001-7776-578X
Jennifer K BurtonSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom.ORCID 0000-0002-4752-6988
Katie McConnellSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom.ORCID 0009-0007-5842-6580
Kalliopi MavromatiSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom.ORCID 0000-0002-6600-064X
Hendrik KnocheDepartment of Architecture, Design and Media Technology, Aalborg University, Aalborg, Denmark.ORCID 0000-0003-3950-8453
Robert MikulikInternational Clinical Research Centre, St. Anne's University Hospital in Brno and Health Management Institute, Brno, Czech Republic.ORCID 0000-0002-7458-5166
Terence J QuinnSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom.ORCID 0000-0003-1401-0181

Funding

European UnionHealth and Digital Executive AgencyHorizon Europe research initiative RES-Q+ 101057603
6 · The paper itself

Abstract

introductionStroke survivors describe insufficient access to information post-discharge. We aimed to compare user perceptions of stroke information from third-sector stroke websites with those generated by a widely accessible artificial intelligence (AI) tool and to summarise the attributes of the preferred stroke information formats. PATIENTS AND

methodsUK third-sector stroke websites were searched for materials relevant to 15 questions asked by stroke survivors. ChatGPT-4o was used to generate responses. Stroke professionals (clinicians, researchers), stroke survivors and caregivers reviewed third-sector and AI responses, guessing the source and identifying their preferred text. Participants rated responses on scales of empathy, trustworthiness, reliability, comprehensibility and usefulness and provided justification. Proportions of preference and correct guesses, as well as mean ratings, were compared between groups. Framework analysis was used to identify the attributes of response formats preferred by stroke survivors.

resultsRelevant responses were found for 13 (87%) of 15 questions. Across groups, 60 participants with a mean age of 44 (SD = 14) and 57% females, correctly identified 184/300 (61%) of AI responses, and preferred AI in 123/300 (46%) of the cases. The groups differed in preference, with clinicians being least likely to choose AI (34%), followed by stroke survivors (49%) and researchers (54%). Stroke professionals viewed third-sector responses as more empathetic. The themes of content, structure and tone of responses were described with an emphasis on clarity, conciseness and an approachable tone. DISCUSSION: Artificial intelligence-generated responses to stroke questions were rated positively by stroke survivors and researchers, whereas stroke clinicians were more sceptical.

conclusionWidely accessible generic AI tools have the potential to complement existing stroke information resources.

Indexed as

Artificial IntelligencePamphletsPatient Education as TopicStrokeSurvivorsAdultFemaleHumansMaleMiddle Agedartificial intelligencehealth educationstroke

Identifiers

PMID42024494
PMCPMC13131226

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.