Evidence map›Paper›PMID 39968514›Full record

ArticleSA journal of radiology2025

Perceptions and attitudes towards AI among trainee and qualified radiologists at selected South African training hospitals.

Ayanda I Nciki, Linda T Hlabangana

Abstract read
In one paragraph

Article in SA journal of radiology, 2025. 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
  2. Article
  3. Article
  4. Article
  5. Review
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

2 authors.

Ayanda I NcikiDepartment of Radiology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.ORCID https://orcid.org/0009-0009-0911-0832
Linda T HlabanganaDepartment of Radiology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.ORCID https://orcid.org/0000-0002-2236-9883

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) is transforming industries, but its adoption in healthcare, especially radiology, remains contentious. Objectives: This study evaluated the perceptions and attitudes of trainee and qualified radiologists towards the adoption of AI in practice. Method: A cross-sectional survey using a paper-based questionnaire was completed by trainee and qualified radiologists. Survey questions covered AI knowledge, perceptions, attitudes, and AI training in the registrar programme on a 3-point Likert scale. Results: A total of 100 participants completed the survey; 54% were aged 26-65 years and 61% were female, with none currently using AI in daily radiology practice. The majority (78%) of participants understood the basics and knew the role of AI in radiology. Most knew about AI from media reports (77%) and majority (95%) were never involved in AI training; only 3% of participants had no knowledge of AI at all. Participants agreed that AI could reliably detect pathological conditions (89%), reach reliable diagnosis (89%), improve daily work (78%), and 89% favoured AI practice; 89% believed that in the future, machine learning will not be independent of the radiologist. Participants were willing to learn (98%) and contribute towards advancing AI software (97%) and agreed that AI will improve the registrars' programme (97%), also noting that AI applications are as important as medical skills (87%). Conclusion: The findings suggest AI in radiology is in its infancy, with a need for educational programmes to upskill radiologists. Contribution: Participants were positive about AI implementation in practice and in the registrar learning programme.

Indexed as

artificial intelligenceattitudesperceptionsqualified radiologisttrainee radiologist

Identifiers

PMID39968514
PMCPMC11830846

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.