Evidence map›Paper›PMID 37153404›Full record

ArticleHeliyon2023

Perspectives of radiologists in Ghana about the emerging role of artificial intelligence in radiology.

Emmanuel Kobina Mesi Edzie, Klenam Dzefi-Tettey, Abdul Raman Asemah, Edmund Kwakye Brakohiapa, Samuel Asiamah, Frank Quarshie, Adu Tutu Amankwa, Amrit Raj, Obed Nimo, Evans Boadi and 5 more

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Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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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

15 authors.

Emmanuel Kobina Mesi EdzieDepartment of Medical Imaging, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana.
Klenam Dzefi-TetteyDepartment of Radiology, Korle Bu Teaching Hospital, 1 Guggisberg Avenue, Accra, Ghana.
Abdul Raman AsemahDepartment of Medical Imaging, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana.
Edmund Kwakye BrakohiapaDepartment of Radiology, University of Ghana Medical School, Accra, Ghana.
Samuel AsiamahDepartment of Radiology, Korle Bu Teaching Hospital, 1 Guggisberg Avenue, Accra, Ghana.
Frank QuarshieAfrican Institute for Mathematical Sciences (AIMS), Summerhill Estate, East Legon Hills, Santoe, Accra, Ghana.
Adu Tutu AmankwaDepartment of Radiology, School of Medical Sciences, College of Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Amrit RajDepartment of Pediatrics, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana.
Obed NimoDepartment of Imaging Technology and Sonography, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana.
Evans BoadiDepartment of Radiology, Korle Bu Teaching Hospital, 1 Guggisberg Avenue, Accra, Ghana.
Joshua Mensah KpobiDepartment of Radiology, Korle Bu Teaching Hospital, 1 Guggisberg Avenue, Accra, Ghana.
Richard Ato EdzieDepartment of Medical Imaging, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana.
Bernard OseiAfrican Institute for Mathematical Sciences (AIMS), Summerhill Estate, East Legon Hills, Santoe, Accra, Ghana.
Veronica TurksonDepartment of Medical Imaging, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana.
Henry KusodziDepartment of Medical Imaging, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The integration of Artificial Intelligence (AI)-based technologies in medicine is advancing rapidly especially in the field of radiology. This however, is at a slow pace in Africa, hence, this study to evaluate the perspectives of Ghanaian radiologists. Methods: Data for this cross-sectional prospective study was collected between September and November 2021 through an online survey and entered into SPSS for analysis. A Mann-Whitney Results: The study comprised 77 radiologists, with more males (71.4%). 97.4% were aware of the concept of AI, with their initial exposure via conferences (42.9%). The majority of the respondents had average awareness (36.4%) and below average expertise (44.2%) in radiological AI usage. Most of the participants (54.5%) stated, they do not use AI in their practices. The respondents disagreed that AI will ultimately replace radiologists in the near future (average Likert score = 3.49, SD = 1.096) and that AI should be an integral part of the training of radiologists (average Likert score = 1.91, SD = 0.830). Conclusion: Although the radiologists had positive opinions about the capabilities of AI, they exhibited an average awareness of and below average expertise in the usage of AI applications in radiology. They agreed on the potential life changing impact of AI and were of the view that AI will not replace radiologists but serve as a complement. There was inadequate radiological AI infrastructure in Ghana.

Indexed as

Artificial intelligenceGhanaMachine learningPerceptionRadiology

Identifiers

PMID37153404
PMCPMC10160753

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.