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<p>This study examines AI information literacy among healthcare professionals and identifies differences across professional roles. An online survey was administered to 390 healthcare professionals, including 22.3% medical doctors, 42.1% nurses, and 35.6% health professionals. AI information literacy was measured using the AI Information Literacy Scale (AILIS), which captures perceived confidence across four dimensions: process/create, assess, retrieve, and ethics. Findings indicated that across AILIS dimensions, participants reported the greatest confidence in retrieving information with AI and the least confidence in critically assessing AI-generated outputs. Medical doctors scored significantly higher than nurses and health professionals on process/create and retrieve. Across the main regression models, five predictors were included: age, perceived AI knowledge, frequency of AI use, number of AI tools used, and self-perceived understanding of how AI tools work. Self-perceived understanding showed the strongest association with all four AI information literacy dimensions. The models explained between 27.3% and 48.3% of the variance. Age showed small negative associations in most models. Self-reported AI information literacy among healthcare professionals appeared uneven across dimensions, with relatively lower confidence in critical assessment. GenAI integration in healthcare should therefore be approached as a literacy and training challenge, with particular attention to evaluation skills and reflective understanding of AI outputs.</p>

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AI information literacy in healthcare context: profiles and associated variables among healthcare professionals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月27日

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  • Lilach Alon,
  • Inbar Levkovich

摘要

This study examines AI information literacy among healthcare professionals and identifies differences across professional roles. An online survey was administered to 390 healthcare professionals, including 22.3% medical doctors, 42.1% nurses, and 35.6% health professionals. AI information literacy was measured using the AI Information Literacy Scale (AILIS), which captures perceived confidence across four dimensions: process/create, assess, retrieve, and ethics. Findings indicated that across AILIS dimensions, participants reported the greatest confidence in retrieving information with AI and the least confidence in critically assessing AI-generated outputs. Medical doctors scored significantly higher than nurses and health professionals on process/create and retrieve. Across the main regression models, five predictors were included: age, perceived AI knowledge, frequency of AI use, number of AI tools used, and self-perceived understanding of how AI tools work. Self-perceived understanding showed the strongest association with all four AI information literacy dimensions. The models explained between 27.3% and 48.3% of the variance. Age showed small negative associations in most models. Self-reported AI information literacy among healthcare professionals appeared uneven across dimensions, with relatively lower confidence in critical assessment. GenAI integration in healthcare should therefore be approached as a literacy and training challenge, with particular attention to evaluation skills and reflective understanding of AI outputs.