Evidence map›Paper›PMID 33586361›Full record

ArticleJournal of medical radiation sciences2021

Radiographers' perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study.

Benard O Botwe, William K Antwi, Samuel Arkoh, Theophilus N Akudjedu

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Article in Journal of medical radiation sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

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

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  14. Artificial Intelligence in Medicine and Dentistry.Acta stomatologica Croatica · 2023
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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

4 authors.

Benard O BotweDepartment of Radiography, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Accra, Ghana.ORCID https://orcid.org/0000-0002-0477-640X
William K AntwiDepartment of Radiography, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Accra, Ghana.ORCID https://orcid.org/0000-0002-6726-2342
Samuel ArkohDepartment of Radiography, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Accra, Ghana.
Theophilus N AkudjeduDepartment of Medical Science & Public Health, Faculty of Health & Social Sciences, Institute of Medical Imaging & Visualisation, Bournemouth University, Poole, UK.ORCID https://orcid.org/0000-0003-2423-6897

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe integration of artificial intelligence (AI) systems into medical imaging is advancing the practice and patient care. It is thought to further revolutionise the entire field in the near future. This study explored Ghanaian radiographers' perspectives on the integration of AI into medical imaging.

methodsA cross-sectional online survey of registered Ghanaian radiographers was conducted within a 3-month period (February-April, 2020). The survey sought information relating to demography, general perspectives on AI and implementation issues. Descriptive and inferential statistics were used for data analyses.

resultsA response rate of 64.5% (151/234) was achieved. Majority of the respondents (n = 122, 80.8%) agreed that AI technology is the future of medical imaging. A good number of them (n = 131, 87.4%) indicated that AI would have an overall positive impact on medical imaging practice. However, some expressed fears about AI-related errors (n = 126, 83.4%), while others expressed concerns relating to job security (n = 35, 23.2%). High equipment cost, lack of knowledge and fear of cyber threats were identified as some factors hindering AI implementation in Ghana.

conclusionsThe radiographers who responded to this survey demonstrated a positive attitude towards the integration of AI into medical imaging. However, there were concerns about AI-related errors, job displacement and salary reduction which need to be addressed. Lack of knowledge, high equipment cost and cyber threats could impede the implementation of AI in medical imaging in Ghana. These findings are likely comparable to most low resource countries and we suggest more education to promote credibility of AI in practice.

Indexed as

Allied Health PersonnelArtificial IntelligenceCross-Sectional StudiesGhanaHumansRadiographyArtificial intelligenceGhanamedical Imagingperspectivesradiographer

Identifiers

PMID33586361
PMCPMC8424310

What OpenQuestion holds

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LicenceCC BY-NC
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.