Evidence map›Paper›PMID 42243358›Full record

ArticleScientific reports2026

Dual dimensions of artificial intelligence use among medical academia: related knowledge, attitudes and ethical concerns, a national survey, 2025.

Doaa Ibrahim Omar, Salman Althobaiti, Basma M Hani, Shaima Adel Elsayed Ali, Mai Magdy Anwer

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Doaa Ibrahim OmarDepartment of Community, Environmental and Occupational Medicine, Faculty of Medicine, Benha University, Benha, Egypt.ORCID https://orcid.org/0000-0003-0540-3522
Salman AlthobaitiDepartment of Family, Community and Medical Education, Faculty of Medicine, Taibah University, Madinah, Saudi Arabia.ORCID https://orcid.org/0000-0003-3885-6325
Basma M HaniDepartment of Community, Environmental and Occupational Medicine, Faculty of Medicine, Benha University, Benha, Egypt.ORCID https://orcid.org/0000-0001-5750-7556
Shaima Adel Elsayed AliDepartment of Community, Environmental and Occupational Medicine, Faculty of Medicine, Benha University, Benha, Egypt.
Mai Magdy AnwerDepartment of Community, Environmental and Occupational Medicine, Faculty of Medicine, Benha University, Benha, Egypt. Mai.saber@fmed.bu.edu.eg.ORCID https://orcid.org/0000-0001-6161-8072

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

AI integration into medical education and practice has its benefits and risks. This national web-based cross-sectional survey on Egyptian medical staff and students aimed to assess knowledge, attitudes, and concerns regarding the use of AI. This study comprised 2765 medical students and 500 medical staff, with a mean age of 20.8 and 29.9 years, respectively, and higher percentages of females among both groups. Medical students demonstrated satisfactory knowledge of AI compared to medical staff (p < 0.001). Unfortunately, the majority of both groups (80.4% of staff and 81.6% of students) expressed negative attitudes toward AI use. Male participants had significantly higher attitude scores than females in both groups. Knowledge score and gender were significant predictors of attitude towards AI among medical staff (p<0.005), while gender was a significant predictor of attitude scores among medical students (p < 0.001). The total AI usage score in this study was higher among students than staff, particularly for idea generation. Medical staff demonstrated a slightly higher total concern score regarding the use of AI in medical education and practice compared with students. The results emphasize the necessity of engagement, focused education and training, compatible solutions, uniform standards, guidelines, and the smooth incorporation of AI into medical education and clinical practice.

Indexed as

Artificial IntelligenceEducation, MedicalHealth Knowledge, Attitudes, PracticeStudents, MedicalAcademiaAdultAttitude of Health PersonnelCross-Sectional StudiesEgyptFemaleHumansMaleSurveys and QuestionnairesYoung AdultArtificial intelligenceAttitudeConcernsKnowledgeMedical educationMedical practice

Identifiers

PMID42243358
PMCPMC13237062

What OpenQuestion 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 OpenQuestion graph.