Evidence map›Paper›PMID 40693185›Full record

ArticleJournal of medical education and curricular development

Attitudes and Awareness of Medical Students Toward Teleradiology and the Application of Artificial Intelligence in Diagnostic Radiology: A Cross-Sectional Study.

Afnan Almuhanna, Danyah Almohsen, Deem AlSultan, Deemah Alhuraish, Farah AlRatrout, Rabab Alzanadi, Rana Abbas

Abstract read
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Article in Journal of medical education and curricular development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

7 authors.

Afnan AlmuhannaImam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Danyah AlmohsenImam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Deem AlSultanImam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Deemah AlhuraishImam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Farah AlRatroutImam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Rabab AlzanadiImam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Rana AbbasBatterjee Medical College, Jeddah, Saudi Arabia.ORCID https://orcid.org/0009-0001-7568-7615

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rationale and Objectives: Teleradiology and artificial intelligence (AI) have emerged as important tools for facilitating healthcare workers in diagnosing, treating, and preventing diseases. They can also be used in the analysis of X-ray images, magnetic resonance imaging, and computed tomography scans. This study delves into the perceptions of medical students at Imam Abdulrahman University (IAU) regarding the role of AI and teleradiology. The findings also shed light on the opinions of medical students at the IAU regarding the potential augmentation and replacement of radiologists with AI. Materials and Methods: A cross-sectional study was conducted in August 2023 among 292 medical students and interns at Imam Abdulrahman bin Faisal University (IAU), Eastern region, Saudi Arabia, with a response rate of 65.2%. Data were collected using an online self-administered questionnaire. The Chi-square test was applied to evaluate the association between demographic factors and perceptions of AI and teleradiology. Since the study was conducted at a single institution, the findings may not be generalizable to all medical students in Saudi Arabia. Students from other universities with different curricula and exposure to AI/teleradiology may have different perceptions. Future multicenter studies are recommended to provide a broader perspective. Additionally, self-reported data are susceptible to response bias, as participants may provide answers they perceive as socially desirable rather than their true opinions. To minimize this, the questionnaire was anonymous, and students were encouraged to answer honestly. However, the possibility of bias cannot be eliminated. Results: The study concluded that 65.2% believed that radiologists should embrace AI. Moreover, 56.1% agreed that the AI would augment the radiologist's capability and efficiency. However, 46.2% believed that teleradiology could replace radiologists working at the hospital, and 42.5% agreed that the impact of AI alone would reduce the number of radiologists needed in the field. Conclusion: Medical students expressed different opinions on the role of AI in augmenting or replacing radiologists. As for teleradiology, the results were promising as students showed interest and optimistic views. Understanding and addressing these perceptions from our future physicians is crucial for developing a strong radiology workforce capable of navigating the transformative journey of a more technological healthcare environment.

Indexed as

artificial intelligencediagnostic radiologymedical studentstelemedicineteleradiology

Identifiers

PMID40693185
PMCPMC12277554

What OpenQuestion holds

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LicenceCC BY-NC
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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.