Evidence map›Paper›PMID 40442679›Full record

ArticleBMC medical education2025

Artificial intelligence in focus: assessing awareness and perceptions among medical students in three private Syrian universities.

Hamdah Hanifa, Mohammad Atia, Rawan Daboul, Ahmad Abdul Hakim Alhamid, Aya Alayyoubi, Hiam Alhaj Naima, Deema Alkassar, Murhaf Ghassan Nabhan, Basil Alsaleh, Farris Abdula

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

  1. Pooled it
  2. 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

10 authors.

Hamdah Hanifa *Faculty of Medicine, University of Kalamoon, Al-Nabk, Syria. hamdahhanifa@gmail.com.ORCID http://orcid.org/0000-0003-2970-7379
Mohammad Atia *Faculty of Medicine, Al_Sham Private University, Damascus, Syria.ORCID http://orcid.org/0009-0005-3795-403X
Rawan DaboulFaculty of Medicine, Homs University, Homs, Syria.ORCID http://orcid.org/0009-0006-7455-5991
Ahmad Abdul Hakim AlhamidFaculty of Medicine, Al-Hawash Private University, Homs, Syria.ORCID http://orcid.org/0009-0002-6478-314X
Aya AlayyoubiFaculty of Medicine, University of Aleppo, Aleppo, Syria.
Hiam Alhaj NaimaFaculty of Medicine, University of Southampton, Southampton, UK.
Deema AlkassarFaculty of Medicine, University of Kalamoon, Al-Nabk, Syria.
Murhaf Ghassan NabhanFaculty of Medicine, Al_Sham Private University, Damascus, Syria.
Basil AlsalehFaculty of Medicine, Tbilisi State Medical University, Tbilisi, Georgia.ORCID http://orcid.org/0009-0002-6614-1204
Farris AbdulaDepartment of Ophthalmology, Medizinische Universität Lausitz- Carl Thiem, Cottbus, Germany.ORCID http://orcid.org/0009-0007-6698-9455

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) has gained significant attention and progress in various scientific fields, especially medicine. Since its introduction in the 1950s, AI has advanced remarkably, supporting innovations like diagnostic tools and healthcare technologies. Despite these developments, challenges such as ethical concerns and limited integration in regions like Syria emphasize the importance of increasing awareness and conducting more targeted studies.

methodsA cross-sectional study was conducted to evaluate medical students' preparedness and readiness to use AI technologies in the medical field using the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS_MS). The scale comprises 22 items divided into 4 domains: ethics, vision, ability, and cognition, with responses rated on a five-point Likert scale, higher scores indicate greater readiness. Data were collected through electronic and paper questionnaires distributed over a period of 20 days.

resultsThe study included 564 medical students from various Syrian universities, of whom 77.8% demonstrated awareness of AI in the medical field. Significant differences in AI awareness were observed based on academic GPA (p = 0.035) and income level (p = 0.016), with higher awareness among students with higher GPA and income levels. Statistically significant differences were found between students aware of AI and those unaware, as well as between students with experience using AI and those without, across all domains of readiness, including cognition (t = -10.319, p < 0.001), ability (t = -11.519, p < 0.001), vision (t = -6.387, p < 0.001), ethics (t = -7.821, p < 0.001), and the overall readiness score (t = -11.354, p < 0.001).

conclusionIntegrating AI into medical education is essential for advancing healthcare in developing countries like Syria. Providing incentives and fostering a culture of continuous learning will equip medical students to leverage AI's benefits while mitigating its drawbacks.

Indexed as

Artificial IntelligenceAwarenessStudents, MedicalAdultCross-Sectional StudiesEducation, Medical, UndergraduateFemaleHumansMaleSurveys and QuestionnairesSyriaUniversitiesYoung AdultArtificial intelligenceDeep learningMachine learningMAIRS_MSMedical studentsSyria

Identifiers

PMID40442679
PMCPMC12123986

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.