Evidence map›Paper›PMID 40437443›Full record

ArticleBMC medical education2025

Medical students' knowledge, attitudes, and practices toward generative artificial intelligence in Egypt 2024: a Cross-Sectional study.

Omar A Ghanem, Abdelmonam M Hagag, Muhammed E Kormod, Mennatullah A El-Refaay, Alaa Mahmoud Khedr, Omnia M Abozaid, Karim M Abdelmoaty, Mona S Hamed

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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 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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

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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

8 authors.

Omar A GhanemFaculty of Medicine, Zagazig University, Zagazig, Egypt. omar.g7anem@gmail.com.ORCID https://orcid.org/0009-0009-1966-3705
Abdelmonam M HagagFaculty of Medicine, Zagazig University, Zagazig, Egypt.ORCID https://orcid.org/0009-0008-1392-3505
Muhammed E KormodFaculty of Medicine, Al-Azhar University, Madinat Nasr, Egypt.ORCID https://orcid.org/0009-0004-9036-3338
Mennatullah A El-RefaayFaculty of Medicine, October 6 University, 6Th of October, Giza, Egypt.ORCID https://orcid.org/0009-0006-2155-5082
Alaa Mahmoud KhedrFaculty of Medicine, Zagazig University, Zagazig, Egypt.ORCID https://orcid.org/0009-0003-6213-4936
Omnia M AbozaidFaculty of Medicine, Minya University, Minya, Egypt.ORCID https://orcid.org/0009-0003-9841-0146
Karim M AbdelmoatyFaculty of Medicine, Zagazig University, Zagazig, Egypt.ORCID https://orcid.org/0009-0007-7494-6682
Mona S HamedCommunity Medicine Department, Faculty of Medicine, Zagazig University, Zagazig, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenerative artificial intelligence (generative AI) has revolutionized the healthcare system for medical students and their educational process worldwide. Several studies assessed its efficacy in improving medical studies learning, and others assessed medical students' knowledge and attitudes about generative AI. So, we aim to determine the knowledge, attitudes, and practices of generative AI among medical students in Egypt.

methodsIn 2024, a cross-sectional study using a semi-structured questionnaire was performed, targeting 423 students as a convenient sample from 10 selected Egyptian universities by simple random sampling. The questionnaire was distributed via online platforms such as WhatsApp, Facebook, X, Telegram, and Messenger. Data about sociodemographic characteristics, students' general knowledge about generative AI and its limitations, and their attitudes and practices, respectively, were collected.

resultsAbout 2\3 (61.5%) of the studied students had satisfactory knowledge about generative AI and (44.7%) had a positive attitude. Males demonstrated significantly higher knowledge than females (69.3% vs. 55%, P-value = 0.003). Knowledge level also differs significantly by university (P-value < 0.001), with Suez-Canal and 6th October university students achieving the highest satisfactory scores. Key significant predictors that affect knowledge level include gender, university and study phase according to binary logistic regression results. Furthermore, there is a positive correlation between satisfactory knowledge and Practice level (r = 0.303, P < 0.001). Finally, most students use generative AI to check Grammar and prepare their homework or assignments. CONCLUSION AND RECOMMENDATIONS: There is moderate knowledge and positive attitude about AI that were reflected in medical students' practice. However, knowledge about AI influenced by gender, university and study phase. Importantly, better knowledge significantly correlates with greater generative AI practice. Offering specialized courses or faculty modules about AI enhance awareness among Egyptian medical students. Such initiatives will enable them to gain knowledge and apply it effectively and wisely.

Indexed as

Artificial IntelligenceHealth Knowledge, Attitudes, PracticeStudents, MedicalAdultCross-Sectional StudiesEducation, Medical, UndergraduateEgyptFemaleGenerative Artificial IntelligenceHumansMaleSurveys and QuestionnairesYoung AdultArtificial intelligenceAttitudesGenerative AIKnowledgeMedical studentsPractices

Identifiers

PMID40437443
PMCPMC12117742

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.