Scholarlink

Objectives <p>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.</p> Methods <p>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.</p> Results <p>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 (<i>P</i> = 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%).</p> Conclusions <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月21日

查看全文
  • Rahma Al Kindi,
  • Asma Al Salmani,
  • Rahma Al Hadhrami,
  • Juman Al Shaqsi,
  • Arwa Al Hadhrami,
  • Sami Al Hatmi,
  • Maryam Al Maashani

摘要

Objectives

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.

Methods

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.

Results

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%).

Conclusions

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.