Evidence map›Paper›PMID 33367198›Full record

ArticleBJR open2020

Artificial intelligence in radiology: does it impact medical students preference for radiology as their future career?

Abdulmajeed Bin Dahmash, Mohammed Alabdulkareem, Aljabriyah Alfutais, Ahmed M Kamel, Feras Alkholaiwi, Shaker Alshehri, Yousof Al Zahrani, Mohammed Almoaiqel

Abstract read
In one paragraph

Article in BJR open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

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

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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

8 authors.

Abdulmajeed Bin DahmashCollege of Medicine, Imam Mohammad ibn Saud Islamic University, Riyadh, Saudi Arabia.ORCID https://orcid.org/0000-0002-7171-1307
Mohammed AlabdulkareemNeuroradiology Division, Department of Medical Imaging, King Abdulaziz Medical City & King Abdullah Specialized Children's Hospital, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia.
Aljabriyah AlfutaisVascular and Interventional Radiology Unit, Department of Medical Imaging, King Abdulaziz Medical City & King Abdullah Specialized Children's Hospital, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia.
Ahmed M KamelDepartment of Clinical Pharmacy, Faculty of Pharmacy, Cairo University, Cairo, Egypt.
Feras AlkholaiwiCollege of Medicine, Imam Mohammad ibn Saud Islamic University, Riyadh, Saudi Arabia.
Shaker AlshehriVascular and Interventional Radiology Unit, Department of Medical Imaging, King Abdulaziz Medical City & King Abdullah Specialized Children's Hospital, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia.
Yousof Al ZahraniVascular and Interventional Radiology Unit, Department of Medical Imaging, King Abdulaziz Medical City & King Abdullah Specialized Children's Hospital, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia.
Mohammed AlmoaiqelVascular and Interventional Radiology Unit, Department of Medical Imaging, King Abdulaziz Medical City & King Abdullah Specialized Children's Hospital, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo test medical students' perceptions of the impact of artificial intelligence (AI) on radiology and the influence of these perceptions on their choice of radiology as a lifetime career.

methodsA cross-sectional multicenter survey of medical students in Saudi Arabia was conducted in April 2019.

resultsOf the 476 respondents, 34 considered radiology their first specialty choice, 26 considered it their second choice, and 65 considered it their third choice. Only 31% believed that AI would replace radiologists in their lifetime, while 44.8% believed that AI would minimize the number of radiologists needed in the future. Approximately 50% believed they had a good understanding of AI; however, when knowledge of AI was tested using five questions, on average, only 22% of the questions were answered correctly. Among the respondents who ranked radiology as their first choice, 58.8% were anxious about the uncertain impact of AI on radiology. The number of respondents who ranked radiology as one of their top three choices increased by 14 when AI was not a consideration. Radiology conferences and the opinions of radiologists had the most influence on the respondents' preferences for radiology.

conclusionThe worry that AI might displace radiologists in the future had a negative influence on medical students' consideration of radiology as a career. Academic radiologists are encouraged to educate their students about AI and its potential impact when students are considering radiology as a lifetime career choice. ADVANCES IN KNOWLEDGE: Rapid advances of AI in radiology will certainly impact the specialty, the concern of AI impact on radiology had negative influence in our participants and investing in AI education and is highly recommended.

Identifiers

PMID33367198
PMCPMC7748985

What OpenQuestion holds

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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.