Knowledge, attitude, and perception of artificial intelligence among medical residents in Oman: readiness for clinical practice

Abstract

This 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. A 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. Of 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%). Postgraduate 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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