Evidence map›Paper›PMID 36939823›Full record

ArticleJMIR medical education2023

Changes in Radiology Due to Artificial Intelligence That Can Attract Medical Students to the Specialty.

David Shalom Liu, Kamil Abu-Shaban, Safwan S Halabi, Tessa Sundaram Cook

Abstract read
In one paragraph

Article in JMIR medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
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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.

David Shalom LiuUniversity of Toledo College of Medicine and Life Sciences, Toledo, OH, United States.ORCID https://orcid.org/0000-0002-1279-9580
Kamil Abu-ShabanUniversity of Toledo College of Medicine and Life Sciences, Toledo, OH, United States.ORCID https://orcid.org/0000-0001-5816-4735
Safwan S HalabiDepartment of Medical Imaging, Ann & Robert H Lurie Children's Hospital of Chicago, Chicago, IL, United States.ORCID https://orcid.org/0000-0003-1317-984X
Tessa Sundaram CookDepartment of Radiology, Hospital of the University of Pennsylvania, Pennsylvania, PA, United States.ORCID https://orcid.org/0000-0002-6007-7267

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The role of artificial intelligence (AI) in radiology has grown exponentially in the recent years. One of the primary worries by medical students is that AI will cause the roles of a radiologist to become automated and thus obsolete. Therefore, there is a greater hesitancy by medical students to choose radiology as a specialty. However, it is in this time of change that the specialty needs new thinkers and leaders. In this succinct viewpoint, 2 medical students involved in AI and 2 radiologists specializing in AI or clinical informatics posit that not only are these fears false, but the field of radiology will be transformed in such a way due to AI that there will be novel reasons to choose radiology. These new factors include greater impact on patient care, new space for innovation, interdisciplinary collaboration, increased patient contact, becoming master diagnosticians, and greater opportunity for global health initiatives, among others. Finally, since medical students view mentorship as a critical resource when deciding their career path, medical educators must also be cognizant of these changes and not give much credence to the prevalent fearmongering. As the field and practice of radiology continue to undergo significant change due to AI, it is urgent and necessary for the conversation to expand from expert to expert to expert to student. Medical students should be encouraged to choose radiology specifically because of the changes brought on by AI rather than being deterred by it.

Indexed as

AIartificial intelligenceautomatedcareclinical informaticsinnovationmedical educationmedical studentspatientradiologistradiologyresidencystudents

Identifiers

PMID36939823
PMCPMC10131993

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.