Evidence map›Paper›PMID 39765931›Full record

ArticleHealthcare (Basel, Switzerland)2024

Readiness to Embrace Artificial Intelligence Among Medical Students in Saudi Arabia: A National Survey.

Abeer Al Shahrani, Norah Alhumaidan, Zeena AlHindawi, Abdullah Althobaiti, Khalid Aloufi, Rasil Almughamisi, Ahad Aldalbahi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 3 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

18 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Medical Students' Readiness for Medical Artificial Intelligence (AI).Inquiry : a journal of medical care organization, provision and financing
    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

7 authors.

Abeer Al ShahraniFamily and Community Medicine Department, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.ORCID 0000-0003-1319-8936
Norah AlhumaidanCollege of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.ORCID 0009-0003-6240-481X
Zeena AlHindawiCollege of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.ORCID 0009-0005-2980-5501
Abdullah AlthobaitiCollege of Medicine, Taif University, Taif 21944, Saudi Arabia.ORCID 0009-0005-9485-844X
Khalid AloufiCollege of Medicine, Northern Border University, Arar 73213, Saudi Arabia.ORCID 0009-0003-2316-2803
Rasil AlmughamisiCollege of Medicine, Taibah University, Al Madinah Al Munawwarah 42353, Saudi Arabia.ORCID 0000-0002-4971-4966
Ahad AldalbahiCollege of Medicine, King Faisal University, Hofuf 31982, Saudi Arabia.ORCID 0009-0008-3290-1379

Funding

Princess Nourah bint Abdulrahman University PNURSP2024R289
6 · The paper itself

Abstract

BACKGROUND/

objectivesArtificial intelligence (AI) is rapidly reshaping healthcare, offering transformative potential for diagnostics, treatment, and patient management. Despite its growing significance, there is limited integration of AI education in medical curricula, raising concerns about the readiness of future healthcare professionals to utilize AI technologies. This study aims to evaluate the readiness of medical students in Saudi Arabia to embrace AI and to assess the current state of AI education, AI Application use, and future perspectives for medical students.

methodsa cross-sectional design was employed. It involved medical students from various regions of Saudi Arabia. Data were collected using an anonymous, online, structured, and validated tool from previous studies. The survey included sociodemographic information, details on AI education, the usage of AI applications, intended specialties, and a Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). The data were extracted and revised in an Excel sheet. Statistical analysis was conducted using the IBM SPSS computer program with appropriate statistical tests.

resultsThis study enrolled 572 medical students, with a mean age of 21.93 years. Most students were Saudi (99.0%), and 43.7% lived in the western region of Saudi Arabia. Most students attended a government medical college (97.41%), and 64.3% of students were in their clinical years. Only 14.5% of the students had received formal AI education, while 34.3% had participated in extracurricular AI training. The mean (SD) MAIRS-MS score was 68.39 (18.3), with higher scores associated with female students, those from the central region, and those with advanced English and computer technology skills (

conclusionsthere is limited AI education and moderate AI readiness among medical students in Saudi colleges, with significant variability in terms of gender, region, and educational background. These findings underscore the need to integrate AI education into medical curricula to better prepare future physicians for AI-enabled healthcare systems.

Indexed as

artificial intelligencecurriculummedical educationmedical studentsSaudi Arabia

Identifiers

PMID39765931
PMCPMC11727990

What Socratic 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 Socratic graph.