ArticleBMC medical education2026
Knowledge, attitude, and perception of artificial intelligence among medical residents in Oman: readiness for clinical practice.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
3 citing papers in PubMed.
- Self-Reported Knowledge, Attitudes, Perceptions, and Readiness Regarding AI Among Obstetrics and Gynecology Trainees: Cross-Sectional Study.JMIR formative research · 2026Article
- Knowledge, Attitude, and Perception of Artificial Intelligence in Healthcare Among Postgraduate Residents: A Cross-sectional Survey.Oman medical journal · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study is the first to assess the knowledge, attitudes and perceptions of artificial intelligence (AI) among postgraduate medical residents at the Oman Medical Specialty Board (OMSB) in Muscat, Oman and to evaluate their preparedness for integrating AI into clinical practice.
methodsA cross-sectional survey was conducted between September and December 2024 among OMSB residents enrolled in the 2024–2025 academic year. A validated, self-administered digital questionnaire assessed residents’ sociodemographic characteristics, knowledge of AI concepts, and perceptions of AI applications in individual patient care, health systems and population health. Descriptive and inferential statistics were used for data analysis.
resultsOf the 256 respondents (mean age 29.7 ± 2.9 years; 81.3% female), 62.1% demonstrated poor knowledge of AI, 34.8% had acceptable knowledge and only 3.1% were knowledgeable (mean score 14.04 ± 3.77). Familiarity was highest for basic AI terminology but substantially lower for machine learning, deep learning and neural networks. Knowledge scores were significantly associated with gender (P = 0.004). Most residents believed AI was likely to replace or substantially assist in diagnostic imaging (86.7%), documentation (87.5%), preventive care (80.1%) and quality improvement (82.0%), often within 0–10 years. Confidence was markedly lower for empathetic care (30.0%) and psychiatric counselling (36.7%).
conclusionsPostgraduate medical residents in Oman exhibit limited foundational knowledge of AI but express optimism regarding its clinical and administrative applications. Notably, knowledge disparities exist by gender, highlighting the need for targeted structured AI-focused educational interventions to enhance competency and ensure effective integration into healthcare practice.
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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 Socratic graph.