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The integration of artificial intelligence in medical imaging practice: Perspectives of African radiographers.
Botwe, B O; Akudjedu, T N; Antwi, W K; Rockson, P; Mkoloma, S S; Balogun, E O; Elshami, W; Bwambale, J; Barare, C; Mdletshe, S; Yao, B; Arkoh, S.
Afiliação
  • Botwe BO; Department of Radiography, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Box KB143, Korle Bu, Accra, Ghana. Electronic address: sirbenard13@gmail.com.
  • Akudjedu TN; Institute of Medical Imaging & Visualisation, Department of Medical Science & Public Health, Faculty of Health & Social Sciences, Bournemouth University, Bournemouth, UK. Electronic address: takudjedu@bournemouth.ac.uk.
  • Antwi WK; Department of Radiography, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Box KB143, Korle Bu, Accra, Ghana. Electronic address: wkantwi@ug.edu.gh.
  • Rockson P; Department of Medical Imaging, University of Health and Allied Sciences, Ho, Ghana. Electronic address: Prockson2018@sahs.uhas.edu.gh.
  • Mkoloma SS; Ocean Road Cancer Institute, Tanzania. Electronic address: bishopmkoloma@gmail.com.
  • Balogun EO; National Orthopaedic Hospital, Igbobi, Lagos, Nigeria. Electronic address: Bethbalo72@gmail.com.
  • Elshami W; Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, United Arab Emirates. Electronic address: welshami@sharjah.ac.ae.
  • Bwambale J; Society of Radiography of Uganda, Uganda. Electronic address: Bwajoe25@gmail.com.
  • Barare C; Kenyatta National Hospital, Kenya. Electronic address: bsiza1@gmail.com.
  • Mdletshe S; University of Auckland, Faculty of Medical and Health Sciences, Department of Anatomy and Medical Imaging, Auckland, New Zealand. Electronic address: sibusiso.mdletshe@auckland.ac.nz.
  • Yao B; National Institute for Health Technologists' Training (INFAS) Côte d'Ivoire, Department of Medical Imaging and Radiotherapy, Côte d'Ivoire. Electronic address: Kwame_boniface@yahoo.fr.
  • Arkoh S; Department of Radiography, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Box KB143, Korle Bu, Accra, Ghana. Electronic address: sampapaarkoh1997@gmail.com.
Radiography (Lond) ; 27(3): 861-866, 2021 08.
Article em En | MEDLINE | ID: mdl-33622574
ABSTRACT

INTRODUCTION:

The current technological developments in medical imaging are centred largely on the increasing integration of artificial intelligence (AI) into all equipment modalities. This survey assessed the perspectives of African radiographers on the integration of AI in medical imaging in order to offer unique recommendations to support the training of the radiography workforce.

METHODS:

An exploratory cross-sectional online survey of radiographers working within Africa was conducted from March to August 2020. The survey obtained data about their demographics and perspectives on AI implementation and usage. Data obtained were analysed using both descriptive and inferential statistics.

RESULTS:

A total of 1020 valid responses were obtained. Majority of the respondents (n = 883,86.6%) were working in general X-ray departments. Of the respondents, 84.9% (n = 866) indicated that AI technology would improve radiography practice and quality assurance for efficient diagnosis and improved clinical care. Fear of job losses following the implementation of AI was a key concern of most radiographers (n = 625,61.3%).

CONCLUSION:

Generally, radiographers were delighted about the integration of AI into medical imaging, however; there were concerns about job security and lack of knowledge. There is an urgent need for stakeholders in medical imaging infrastructure development and practices in Africa to start empowering radiographers through training programmes, funding, motivational support, and create clear roadmaps to guide the adoption and integration of AI in medical imaging in Africa. IMPLICATION FOR PRACTICE The current study offers unique suggestions and recommendations to support the training of the African radiography workforce and others in similar resource-limited settings to provide quality care using AI-integrated imaging modalities.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Serviço Hospitalar de Radiologia / Inteligência Artificial Tipo de estudo: Diagnostic_studies / Guideline / Observational_studies / Prevalence_studies / Qualitative_research / Risk_factors_studies Limite: Humans Idioma: En Revista: Radiography (Lond) Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Serviço Hospitalar de Radiologia / Inteligência Artificial Tipo de estudo: Diagnostic_studies / Guideline / Observational_studies / Prevalence_studies / Qualitative_research / Risk_factors_studies Limite: Humans Idioma: En Revista: Radiography (Lond) Ano de publicação: 2021 Tipo de documento: Article
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