Scholarlink

Artificial intelligence (AI) is increasingly vital for transforming healthcare, yet its successful integration depends critically on user acceptance. This study provides acceptance of AI in clinical decision-making at the Mohammed VI University Hospital Center in Oujda, Morocco. A quantitative cross-sectional survey was administered to 353 physicians and nurses. Overall, AI acceptance was reported by 67.10% of participants. Statistical analysis - Chi-square test χ2- revealed that acceptance was not associated with socio-demographic or professional characteristics. In contrast, acceptance was significantly linked to trust in AI (p < 0.001), perceived usefulness (p < 0.001), ease of use (p = 0.005), data management confidence (p < 0.001), and patient safety insurance (p < 0.001). As a result, instead of caregivers’ characteristics, user’s perception of utility, safety and operational benefits are regarded as a main influence; leading to an adoption of AI in this study context. Hence, targeted training programs that address operational and ethical concerns must be required for the purpose of facilitating AI integration, which is supported by regulatory and ethical systems.

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

Perceived Utility and Ease of Use as Predictors for AI Acceptance in Clinical Practice

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Ouafae El Ajroudi,
  • Amal Azdimousa,
  • Hajar Chafik,
  • Meryem Arsi,
  • Malak Bounouar,
  • Sara Jelti

摘要

Artificial intelligence (AI) is increasingly vital for transforming healthcare, yet its successful integration depends critically on user acceptance. This study provides acceptance of AI in clinical decision-making at the Mohammed VI University Hospital Center in Oujda, Morocco. A quantitative cross-sectional survey was administered to 353 physicians and nurses. Overall, AI acceptance was reported by 67.10% of participants. Statistical analysis - Chi-square test χ2- revealed that acceptance was not associated with socio-demographic or professional characteristics. In contrast, acceptance was significantly linked to trust in AI (p < 0.001), perceived usefulness (p < 0.001), ease of use (p = 0.005), data management confidence (p < 0.001), and patient safety insurance (p < 0.001). As a result, instead of caregivers’ characteristics, user’s perception of utility, safety and operational benefits are regarded as a main influence; leading to an adoption of AI in this study context. Hence, targeted training programs that address operational and ethical concerns must be required for the purpose of facilitating AI integration, which is supported by regulatory and ethical systems.