Evidence map›Paper›PMID 39981011›Full record

ArticleEthiopian journal of health sciences2024

Assessment of Knowledge, Practice, Perception, and Expectations of Artificial Intelligence in Medical Care among Staff of a Tertiary Hospital.

Aondona David Daniel, Akwaras Nndunno Asheku, Yohanna Stephen, Gyuse Ngueikyor Abraham, De-Kaa Niongun Lawrence Paul, Laadi Terrumun Swende, Ornguga Bamidele Ohiozoje, Grace Nwunuji Rimamnunra, Ngbede Matthew Ocheifa

Abstract read
In one paragraph

Article in Ethiopian journal of health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Aondona David DanielDepartment of Family Medicine. Federal Medical Centre Makurdi. Benue State. Nigeria.
Akwaras Nndunno AshekuDepartment of Family Medicine. Federal Medical Centre Makurdi. Benue State. Nigeria.
Yohanna StephenBingham University Teaching Hospital, Jos. Plateau State. Nigeria.
Gyuse Ngueikyor AbrahamUniversity of Calabar Teaching Hospital, Calabar, Cross River State, Nigeria.
De-Kaa Niongun Lawrence PaulDepartment of Family Medicine. Federal Medical Centre Makurdi. Benue State. Nigeria.
Laadi Terrumun SwendeDepartment of Family Medicine. Federal Medical Centre Makurdi. Benue State. Nigeria.
Ornguga Bamidele OhiozojeDepartment of Family Medicine. Federal Medical Centre Makurdi. Benue State. Nigeria.
Grace Nwunuji RimamnunraDepartment of Epidemiology and Community Health, College of Health Sciences, Benue State University Makurdi, Benue State, Nigeria.
Ngbede Matthew OcheifaDepartment of Family Medicine. Federal Medical Centre Makurdi. Benue State. Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. AI technology has wide applications in biomedicine and has real practical benefits in many medical applications. The aim was to assess the knowledge, practice, perception, and expectations about AI technology among staff of Federal Medical Centre Makurdi Benue state, Nigeria. Methods: This was a descriptive cross-sectional study over a period of three months from March to May 2023. The respondents were 18 years and above. The questionnaire was self-administered employing convenience sampling method to recruit responders. Data analysis was done using SPSS version 20. Result: A total of 384 respondents were recruited. The mean (SD) age of the respondents was 42.3(±11.1). Most were aged 41-50 (34.4%). There were more females (56% (215)). Most of the respondents (69% (264)) attested to knowing AI technology. However, the majority (87% (231)) of the 264 respondents who knew about AI technology did not have in-depth knowledge. Regarding practices, more than half of the respondents (55.3%) did not think AI makes their task easy. The majority of the respondents (90.3%) believed AI technology is essential in the medical field and most of the respondents (12.2%) were expecting to acquire AI technology skills in the future. Conclusion: The in-depth knowledge of AI technology was low. Most of the staff thought that AI technology did not make their task easy although they believe AI is essential in medical field and they expect the acquisition of more skills on AI technology in future.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelHealth Knowledge, Attitudes, PracticeAdultCross-Sectional StudiesFemaleHumansMaleMiddle AgedNigeriaSurveys and QuestionnairesTertiary Care CentersYoung AdultArtificial intelligenceexpectationknowledgeperceptionpractice

Identifiers

PMID39981011
PMCPMC11837799

What OpenQuestion holds

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LicenceCC BY
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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.