Evidence map›Paper›PMID 39664146›Full record

ArticleCureus2024

Familiarity and Applications of Artificial Intelligence in Health Professions Education: Perspectives of Students in a Community-Oriented Medical School.

Khaldoon Al-Roomi, Salman Alzayani, Amer Almarabheh, Mohamed Alqahtani, Fatmah Aldosari, Muneerah Aladwani, Noor Aldeyouli, Rahaf Alhobail, Hany Atwa, Abdelhalim Deifalla

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Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

10 authors.

Khaldoon Al-RoomiDepartment of Family and Community Medicine, Arabian Gulf University, Manama, BHR.
Salman AlzayaniDepartment of Family and Community Medicine, Arabian Gulf University, Manama, BHR.
Amer AlmarabhehDepartment of Family and Community Medicine, Arabian Gulf University, Manama, BHR.
Mohamed AlqahtaniDepartment of Family and Community Medicine, Arabian Gulf University, Manama, BHR.
Fatmah AldosariDepartment of Family and Community Medicine, Arabian Gulf University, Manama, BHR.
Muneerah AladwaniDepartment of Family and Community Medicine, Arabian Gulf University, Manama, BHR.
Noor AldeyouliDepartment of Family and Community Medicine, Arabian Gulf University, Manama, BHR.
Rahaf AlhobailDepartment of Family and Community Medicine, Arabian Gulf University, Manama, BHR.
Hany AtwaDepartment of Medical Education, Arabian Gulf University, Manama, BHR.
Abdelhalim DeifallaDepartment of Anatomy, Arabian Gulf University, Manama, BHR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aim Medical students are expected to be familiar with artificial intelligence (AI) applications in healthcare. This cross-sectional study looked at the attitudes, thoughts, and understanding of healthcare students toward AI. Materials and methods During the academic year 2023-2024, medical students enrolled in the College of Medicine and Health Sciences (CMHS) at the Arabian Gulf University (AGU) were included in this study. A questionnaire was developed to evaluate their understanding and opinions regarding the use of AI in medical training. These data were gathered, categorized, and analyzed using the Statistical Package for Social Sciences (SPSS) version 29 (IBM Corp., Armonk, NY, US). Categorical variables were shown in the form of frequencies and percentages, whereas continuous variables were presented as mean and standard deviation (SD). Chi-square tests were utilized for comparing categorical variables. A p-value of <0.05 was considered statistically significant. Results The study found that n=41 (27%) of medical students are very familiar with AI applications while n=92 (60.5%) are somewhat familiar. Familiarity increases as students progress in their medical education, with senior clinical phase students more familiar than juniors. There was no significant difference in perceptions of AI application among medical phases. Familiarity with research methodology and studies increases familiarity with AI applications. Most students believe AI will have a positive impact on medical education, but perceptions vary by educational phase. Many students support integrating AI into curricula with 67 (44.1%) of students using AI applications, with a higher percentage in pre-clinical phases, likely due to application in research projects in this phase. Concerns were raised about AI impacting the human touch in medical practice and doctor-patient communication, as well as technical challenges faced by students when applying AI. Conclusion Arab Gulf medical students show positive attitudes toward AI applications in medical education. Tailored educational strategies are needed to optimize AI integration in medical practice and address concerns effectively.

Indexed as

arab gulfartificial intelligencelearningmedical educationmedical student

Identifiers

PMID39664146
PMCPMC11632895

What OpenQuestion holds

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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.