Evidence map›Paper›PMID 42524542›Full record

ArticleHealth science reports2026

Saudi Medical Students' Perceptions and Attitudes of Integrating Generative Artificial Intelligence Integration in Medical Education: A Cross-Sectional Study.

Fadi Aljamaan, Muhammad Faisal Mubarak, Ibraheem Altamimi, Alaa A Alanteet, Mohammed A Alsalman, Shereen A Dasuqi, Rashid Alballaa, Mohammed I Alarifi, Abdalrhman Al Saadon, Abdulrahman O Alhaqbani and 9 more

Abstract read
In one paragraph

Article in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. 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

19 authors.

Fadi AljamaanCollege of Medicine King Saud University Riyadh Saudi Arabia.
Muhammad Faisal MubarakCollege of Medicine King Saud University Riyadh Saudi Arabia.
Ibraheem AltamimiCollege of Medicine King Saud University Riyadh Saudi Arabia.
Alaa A AlanteetDepartment of Pharmacology and Toxicology, College of Pharmacy King Saud University Riyadh Saudi Arabia.
Mohammed A AlsalmanCollege of Medicine King Saud University Riyadh Saudi Arabia.
Shereen A DasuqiDepartment of Pharmacy, King Khalid University Hospital King Saud University Riyadh Saudi Arabia.
Rashid AlballaaCollege of Medicine King Saud University Riyadh Saudi Arabia.
Mohammed I AlarifiCollege of Medicine King Saud University Riyadh Saudi Arabia.
Abdalrhman Al SaadonCollege of Medicine King Saud University Riyadh Saudi Arabia.
Abdulrahman O AlhaqbaniCollege of Medicine King Saud University Riyadh Saudi Arabia.
Abdulrahman A AlhadlaqCollege of Medicine King Saud University Riyadh Saudi Arabia.
Shirin H AlokayliCollege of Medicine King Saud University Riyadh Saudi Arabia.
Bader N AlrasheedCollege of Medicine King Saud University Riyadh Saudi Arabia.
Sarah I AlkhalifeCollege of Medicine King Saud University Riyadh Saudi Arabia.
Kamran SattarCollege of Medicine King Saud University Riyadh Saudi Arabia.
Amr JamalCollege of Medicine King Saud University Riyadh Saudi Arabia.
Mona SolimanCollege of Medicine King Saud University Riyadh Saudi Arabia.
Khaled SaadDepartment of Pediatrics Assiut University Assiut Egypt.ORCID https://orcid.org/0000-0002-8473-6116
Mohamad-Hani TemsahCollege of Medicine King Saud University Riyadh Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: Generative Artificial Intelligence (GenAI) has catalyzed a transformation in medical education. Understanding learners' perceptions is essential to guide their responsible integration into curricula. Methods: A cross-sectional survey was administered to 1039 undergraduate medical students across Saudi Arabia. A purpose-developed, pilot-tested instrument (Cronbach's Results: Among 1039 participants (64.3% male; median age 22 years [IQR 20-24]), 57.2% (595/1039) reported familiarity with computational language models in medical education, and 70.1% (728/1039; 95% CI: 67.2-72.9) supported curricular integration. A strong majority (86.4%; 898/1039; 95% CI: 84.2-88.4) anticipated impact on the future of medical education. While 73.4% (763/1039; 95% CI: 70.6-76.0) perceived benefit for basic science education, only 41.6% (432/1039; 95% CI: 38.6-44.6) recognized utility in clinical skills training. Only 29.8% (310/1039; 95% CI: 27.0-32.7) considered these tools superior to human instruction. Key concerns included distrust in output reliability (52.6%; 547/1039; 95% CI: 49.5-55.7) and awareness of reference fabrication (64.0%; 665/1039; 95% CI: 61.0-66.9). Conclusion: Saudi medical students express strong interest in GenAI-particularly for basic sciences and simulation-but perceive it as complementary rather than superior to human instruction. Findings reflect learner perceptions, not measured educational effectiveness. Implementation should prioritize reliability, ethical use, and preservation of humanistic competencies.

Indexed as

ChatGPT and digital learningeducational resourcesgenerative artificial intelligenceLLM in higher educationSaudi Arabia AI adoptionundergraduate medical students

Identifiers

PMID42524542
PMCPMC13412539

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.