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

被引：18

作者：

van de Venter, Riaan ^{[1
,2
]}

Skelton, Emily ^{[2
,3
]}

Matthew, Jacqueline ^{[3
,4
]}

Woznitza, Nick ^{[5
,6
]}

Tarroni, Giacomo ^{[7
,8
]}

Hirani, Shashivadan P. ^{[9
]}

Kumar, Amrita ^{[10
]}

Malik, Rizwan ^{[2
,11
]}

Malamateniou, Christina ^{[2
,3
,12
]}

机构：

[1] Nelson Mandela Univ, Fac Hlth Sci, Sch Clin Care Sci, Dept Radiog, Port Elizabeth, South Africa

[2] City Univ London, Sch Hlth & Psychol Sci, Div Midwifery & Radiog, London, England

[3] Kings Coll London, Dept Perinatal Imaging & Hlth, London, England

[4] Guys & St Thomas NHS Fdn Trust, London, England

[5] Univ Coll London Hosp, Radiol Dept, London, England

[6] Canterbury Christ Church Univ, Sch Allied & Publ Hlth Profess, Canterbury, England

[7] City Univ London, Dept Comp Sci, Cit AI, London, England

[8] Imperial Coll London, Dept Comp, BioMedIA, London, England

[9] City Univ London, Ctr Healthcare Innovat Res, London, England

[10] Frimley Hlth NHS Fdn Trust, London, England

[11] Royal Bolton Hosp, Farnworth, England

[12] HESAV Univ, Dept Radiog, Lausanne, Switzerland

来源：

INSIGHTS INTO IMAGING | 2023年 / 14卷 / 01期

关键词：

Artificial intelligence; Radiography; Education; Evaluation; Action research; HEALTH-CARE; TELEHEALTH; INNOVATION; ONLINE;

D O I：

10.1186/s13244-023-01372-2

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

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.MethodologyA 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.

引用

页数：13

共 62 条

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