Evidence map›Paper›PMID 40396072›Full record

ArticleJournal of family medicine and primary care2025

Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Saudi Arabia.

Faisal A Alabbad, Aljohara S Almeneessier, Mohammed Hisham Alshalan, Mohammad N Aljarba

Abstract read
In one paragraph

Article in Journal of family medicine and primary care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

4 authors.

Faisal A AlabbadFamily and Community Medicine Department, King Saud University Medical City, Riyadh, Saudi Arabia.
Aljohara S AlmeneessierFamily and Community Medicine Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Mohammed Hisham AlshalanDepartment of Family Medicine and Polyclinics, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia.
Mohammad N AljarbaCollege of Medicine, King Saud University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) is advancing rapidly across fields, including healthcare, where it is being adopted for diagnostics and patient management. However, research on Saudi Arabian healthcare professionals' understanding and perceptions of AI remains limited. Objectives: This study aims to assess the knowledge, attitude, and practices (KAP) regarding AI among medical students, interns, and residents, identifying educational gaps and perceptions of AI's future in medicine. Methods: A cross-sectional survey was conducted in Riyadh, Saudi Arabia, targeting medical students, interns, and residents. An online questionnaire collected demographic information, as well as participants' knowledge and attitudes towards AI, and their experience with its applications in medicine. Responses were analyzed statistically for any associations. Results: Of 374 responses, 98.4% were aware of AI, though only 50.5% could identify AI subtypes, and 48.9% understood its medical applications. Formal AI education was lacking for 59.4%, despite 81.8% recognizing AI's importance in diagnosis. Concerns about AI's impact on jobs were noted by 77.8%. While 62.6% had used AI in practice, 66.6% found it beneficial. Conclusion: High awareness of AI contrasts with gaps in specific knowledge and formal training. Positive attitudes are tempered by job security concerns. Findings suggest the need for a structured AI curriculum in medical education to improve comprehension and application in healthcare.

Indexed as

Artificial intelligenceattitudedoctorinternknowledgemedical studentpractice

Identifiers

PMID40396072
PMCPMC12088554

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.