Evidence map›Paper›PMID 36735172›Full record

ArticleInsights into imaging2023

Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study.

Riaan van de Venter, Emily Skelton, Jacqueline Matthew, Nick Woznitza, Giacomo Tarroni, Shashivadan P Hirani, Amrita Kumar, Rizwan Malik, Christina Malamateniou

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In one paragraph

Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

9 authors.

Riaan van de VenterDepartment of Radiography, Faculty of Health Sciences, School of Clinical Care Sciences, Nelson Mandela University, Port Elizabeth, South Africa. riaan.vandeventer@mandela.ac.za.
Emily SkeltonDivision of Midwifery and Radiography, School of Health and Psychological Sciences, City, University of London, London, UK.
Jacqueline MatthewDepartment of Perinatal Imaging and Health, King's College London, London, UK.
Nick WoznitzaRadiology Department, University College London Hospitals, London, UK.
Giacomo TarroniCit-AI, Department of Computer Science, City, University of London, London, UK.
Shashivadan P HiraniCentre for Healthcare Innovation Research, City, University of London, London, UK.
Amrita KumarFrimley Health NHS Foundation Trust, London, UK.
Rizwan MalikDivision of Midwifery and Radiography, School of Health and Psychological Sciences, City, University of London, London, UK.
Christina MalamateniouDivision of Midwifery and Radiography, School of Health and Psychological Sciences, City, University of London, London, UK. Christina.Malamateniou@city.ac.uk.ORCID http://orcid.org/0000-0002-2352-8575

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. METHODOLOGY: A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis.

resultsSeven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants' professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences.

conclusionsThe findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses.

Indexed as

Action researchArtificial intelligenceEducationEvaluationRadiography

Identifiers

PMID36735172
PMCPMC9897152

What OpenQuestion holds

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LicenceCC BY
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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.