Evidence map›Paper›PMID 34642789›Full record

ArticlePediatric radiology2022

Artificial intelligence in paediatric radiology: international survey of health care professionals' opinions.

Susan C Shelmerdine, Karen Rosendahl, Owen J Arthurs

Abstract read
PubMed Publisher
In one paragraph

Article in Pediatric radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
–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

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  2. Pooled it
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  4. Artificial intelligence in clinical genetics: Current practice and attitudes among the clinical genetics workforce.Genetics in medicine : official journal of the American College of Medical Genetics · 2026
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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

3 authors.

Susan C ShelmerdineDepartment of Clinical Radiology, Great Ormond Street Hospital for Children, London, WC1N 3JH, UK. susan.shelmerdine@gosh.nhs.uk.ORCID http://orcid.org/0000-0001-6642-9967
Karen RosendahlDepartment of Clinical Medicine, UiT The Arctic University of Norway, Tromsø, Norway.
Owen J ArthursDepartment of Clinical Radiology, Great Ormond Street Hospital for Children, London, WC1N 3JH, UK.

Funding

National Institute for Health Research NIHR-CDF-2017-10-037
6 · The paper itself

Abstract

backgroundThe nature of paediatric radiology work poses several challenges for developing and implementing artificial intelligence (AI) tools, but opinions of those working in the field are currently unknown.

objectiveTo evaluate the attitudes and perceptions toward AI amongst health care professionals working within children's imaging services. MATERIALS AND

methodsA web-based questionnaire was distributed to the membership of several paediatric and general radiological societies over a 4-month period (1 Feb - 31 May 2020). Survey questions covered attitudes toward AI in general, future impacts and suggested areas for development specifically within paediatric imaging.

resultsTwo hundred and forty responses were collected with the majority being from radiologists (159/240, 66.3%; 95% confidence interval [CI] 59.8-72.2%) or allied health care professionals (72/240, 31.3%; 95% CI 25.4-37.5%). Respondents agreed that AI could potentially alert radiologists to imaging abnormalities (148/240, 61.7%; 95% CI 55.2-67.9%) but preferred that results were checked by a human (200/240, 83.3%; 95% CI 78.0-87.8%). The majority did not believe jobs in paediatric radiology would be replaced by AI (205/240, 85.4%; 95% CI 80.3-89.6%) and that the development of AI tools should focus on improved diagnostic accuracy (77/240, 32.1%; 95% CI 26.2-38.4%), workflow efficiencies (60/240, 25.0%; 95% CI 19.7-30.9%) and patient safety (54/240, 22.5%; 95% CI 17.4-28.3%). The majority of European Society of Paediatric Radiology (ESPR) members (67/81, 82.7%; 95% CI 72.7-90.2%) welcomed the idea of a dedicated paediatric radiology AI task force with emphasis on educational events and anonymised dataset curation.

conclusionImaging health care professionals working with children had a positive outlook regarding the use of AI in paediatric radiology, and did not feel their jobs were threatened. Future AI tools would be most beneficial for easily automated tasks and most practitioners welcomed the opportunity for further AI educational activities.

Indexed as

Artificial IntelligenceRadiologyAttitude of Health PersonnelChildHumansRadiologistsSurveys and QuestionnairesArtificial intelligenceChildrenImagingMachine learningPaediatric RadiologyRadiologySurvey

Identifiers

PMID34642789

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.