ArticleBMC medical education2024
Medical students and house officers' perception, attitude and potential barriers towards artificial intelligence in Egypt, cross sectional survey.
Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical Students' Attitudes, Perceptions, and Self-Reported Familiarity With AI in Health Care: Systematic Review and Meta-Analysis.JMIR medical education · 2026Pooled it
- Patient attitudes toward artificial intelligence in Jordanian Healthcare: A cross-sectional survey study.PLOS digital health · 2026Article
- Dual dimensions of artificial intelligence use among medical academia: related knowledge, attitudes and ethical concerns, a national survey, 2025.Scientific reports · 2026Article
- Generative AI and knowledge management in health education: a cross-sectional study of students' digital literacy and attitudes from a developing-country context.BMC medical education · 2026Article
- Health sciences students' attitudes toward artificial intelligence: predictors of ethical awareness, clinical decision-making, and public health perceptions-a cross-sectional study.BMC medical education · 2026Article
- Familiarity, attitude and practice of postgraduate health science students of Pakistan regarding the implication of artificial intelligence in research: an analytical survey.BMC medical education · 2026Article
- Artificial intelligence and ChatGPT literacy among surgical healthcare professionals: knowledge, attitudes, and perceived clinical utility.Frontiers in digital health · 2026Article
- Medical students' perception of AI's role in radiology before and after an AI-focused educational panel: a paired pre-post design.BMC medical education · 2025Article
- Perceptions and Attitudes of Emergency Physicians in Saudi Arabia Regarding Medical Use of Artificial Intelligence.Cureus · 2025Article
- Integrating a knowledge-based artificial intelligence chatbot into nursing training programs: a comparative quasi-experimental study in Egypt and Saudi Arabia.BMC nursing · 2025Article
- Attitudes, Perceptions, and Factors Influencing the Adoption of AI in Health Care Among Medical Staff: Nationwide Cross-Sectional Survey Study.Journal of medical Internet research · 2025Article
- Article
- Perception and awareness of healthcare professionals toward the applications of artificial intelligence in Egyptian healthcare settings.Frontiers in artificial intelligence · 2025Article
- The impact of an artificial intelligence enhancement program on healthcare providers' knowledge, attitudes, and workplace flourishing.Frontiers in public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is one of the sectors of medical research that is expanding the fastest right now in healthcare. AI has rapidly advanced in the field of medicine, helping to treat a variety of illnesses and reducing the number of diagnostic and follow-up errors.
objectiveThis study aims to assess the perception and attitude towards artificial intelligence (AI) among medical students & house officers in Egypt.
methodsAn online cross-sectional study was done using a questionnaire on the Google Form website. The survey collected demographic data and explored participants' perception, attitude & potential barriers towards AI.
resultsThere are 1,346 responses from Egyptian medical students (25.8%) & house officers (74.2%). Most participants have inadequate perception (76.4%) about the importance and usage of AI in the medical field, while the majority (87.4%) have a negative attitude. Multivariate analysis revealed that age is the only independent predictor of AI perception (AOR = 1.07, 95% CI 1.01-1.13). However, perception level and gender are both independent predictors of attitude towards AI (AOR = 1.93, 95% CI 1.37-2.74 & AOR = 1.80, 95% CI 1.30-2.49, respectively).
conclusionThe study found that medical students and house officers in Egypt have an overall negative attitude towards the integration of AI technologies in healthcare. Despite the potential benefits of AI-driven digital medicine, most respondents expressed concerns about the practical application of these technologies in the clinical setting. The current study highlights the need to address the concerns of medical students and house officers towards AI integration in Egypt. A multi-pronged approach, including education, targeted training, and addressing specific concerns, is necessary to facilitate the wider adoption of AI-enabled healthcare.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.