Evidence map›Paper›PMID 40782183›Full record

ArticleInsights into imaging2025

Parental and carer views on the use of AI in imaging for children: a national survey.

Girija Agarwal, Raimat Korede Salami, Lauren Lee, Helena Martin, Lahvanya Shantharam, Kate Thomas, Emily Ashworth, Emma Allan, Ka-Wai Yung, Cato Pauling and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

13 authors.

Girija AgarwalImperial College Healthcare NHS Trust, London, UK. girija178@gmail.com.ORCID http://orcid.org/0000-0001-8909-6560
Raimat Korede SalamiKings College Hospital NHS Foundation Trust, London, UK.
Lauren LeeYoung Persons Advisory Group (YPAG), Great Ormond Street Hospital for Children, London, UK.
Helena MartinGuy's and St Thomas' NHS Foundation Trust, London, UK.
Lahvanya ShantharamSt George's Hospital, London, UK.
Kate ThomasVictoria Hospital, NHS Fife, Kirkcaldy, Scotland, UK.
Emily AshworthDepartment of Clinical Radiology, Great Ormond Street Hospital for Children, London, UK.
Emma AllanDepartment of Clinical Radiology, Great Ormond Street Hospital for Children, London, UK.
Ka-Wai YungWellcome/EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, London, UK.
Cato PaulingUniversity College London, London, UK.
Deidre LeydenYoung Persons Advisory Group (YPAG), Great Ormond Street Hospital for Children, London, UK.
Owen J ArthursDepartment of Clinical Radiology, Great Ormond Street Hospital for Children, London, UK.
Susan Cheng ShelmerdineDepartment of Clinical Radiology, Great Ormond Street Hospital for Children, London, UK.

Funding

Great Ormond Street Hospital Children's Charity (GOSHCC) VS0618NIHR Advanced Fellowship Award NIHR-301322NIHR Career Development Fellowship NIHR-CDF-2017-10-037Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) 203145Z/16/Z, NS/A000050/1Wellcome Trust
6 · The paper itself

Abstract

objectivesAlthough the use of artificial intelligence (AI) in healthcare is increasing, stakeholder engagement remains poor, particularly relating to understanding parent/carer acceptance of AI tools in paediatric imaging. We explore these perceptions and compare them to the opinions of children and young people (CYAP). MATERIALS AND

methodsA UK national online survey was conducted, inviting parents, carers and guardians of children to participate. The survey was "live" from June 2022 to 2023. The survey included questions asking about respondents' views of AI in general, as well as in specific circumstances (e.g. fractures) with respect to children's healthcare.

resultsOne hundred forty-six parents/carers (mean age = 45; range = 21-80) from all four nations of the UK responded. Most respondents (93/146, 64%) believed that AI would be more accurate at interpreting paediatric musculoskeletal radiographs than healthcare professionals, but had a strong preference for human supervision (66%). Whilst male respondents were more likely to believe that AI would be more accurate (55/72, 76%), they were twice as likely as female parents/carers to believe that AI use could result in their child's data falling into the wrong hands. Most respondents would like to be asked permission before AI is used for the interpretation of their child's scans (104/146, 71%). Notably, 79% of parents/carers prioritised accuracy over speed compared to 66% of CYAP.

conclusionParents/carers feel positively about AI for paediatric imaging but strongly discourage autonomous use. Acknowledging the diverse opinions of the patient population is vital in aiding the successful integration of AI for paediatric imaging. CRITICAL RELEVANCE STATEMENT: Parents/carers demonstrate a preference for AI use with human supervision that prioritises accuracy, transparency and institutional accountability. AI is welcomed as a supportive tool, but not as a substitute for human expertise. KEY POINTS: Parents/carers are accepting of AI use, with human supervision. Over half believe AI would replace doctors/nurses looking at bone X-rays within 5 years. Parents/carers are more likely than CYAP to trust AI's accuracy. Parents/carers are also more sceptical about AI data misuse.

Indexed as

Artificial intelligenceMedical imagingPaediatricParentsSurveys and questionnaires

Identifiers

PMID40782183
PMCPMC12335414

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.