Background <p>The growing integration of artificial intelligence in nursing has been accompanied by increased anxiety among nurses, even as they aim to enhance the quality and efficiency of care. Artificial intelligence anxiety (AI anxiety) constitutes another significant factor influencing the decline in nursing work quality and the deterioration of nurses’ physical and mental health. However, AI anxiety among nurses is still not clearly conceptualized and has received limited empirical examination.</p> Objective <p>This study clarifies the concept of AI anxiety among nurses, offering a comprehensive understanding for nursing managers and researchers to support relevant measurements and interventions.</p> Data sources <p>This study retrieved studies from inception to July 2026 across PubMed, CINAHL (via EBSCO), ELSEVIER ScienceDirect, ProQuest, Embase, Web of Science, Scopus, China National Knowledge Infrastructure (CNKI), China Wanfang Database, and China VIP Database. Relevant references were tracked. This systematic database search aimed to comprehensively collect various research and academic literature to provide evidence and support for conceptual analysis. A total of 31 articles were included in the review.</p> Methods <p>This study employed Walker and Avant’s concept analysis method.</p> Results <p>A total of 31 papers were included in the study. The four primary characteristics of nurse AI anxiety are: Technical concerns, patient safety concerns, perceived ethical burden, and sense of occupational devaluation. The antecedents are categorized into individual, environmental and related to artificial intelligence. The consequences of AI anxiety are distinguished into individual-level and hospital-level impacts.</p> Conclusion <p>This study provides a comprehensive understanding of the concept of AI anxiety among nurses by outlining its antecedents, attributes, and consequences. The conceptualization of AI anxiety will facilitate future research aimed at establishing effective prevention strategies.</p>

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A concept analysis of artificial intelligence anxiety among nurses based on Walker and Avant’s method

  • Xin Luo,
  • Chunxiu Zhang,
  • Jiajia Xia,
  • Fangmin Li,
  • Qi Ao,
  • Peili Xu

摘要

Background

The growing integration of artificial intelligence in nursing has been accompanied by increased anxiety among nurses, even as they aim to enhance the quality and efficiency of care. Artificial intelligence anxiety (AI anxiety) constitutes another significant factor influencing the decline in nursing work quality and the deterioration of nurses’ physical and mental health. However, AI anxiety among nurses is still not clearly conceptualized and has received limited empirical examination.

Objective

This study clarifies the concept of AI anxiety among nurses, offering a comprehensive understanding for nursing managers and researchers to support relevant measurements and interventions.

Data sources

This study retrieved studies from inception to July 2026 across PubMed, CINAHL (via EBSCO), ELSEVIER ScienceDirect, ProQuest, Embase, Web of Science, Scopus, China National Knowledge Infrastructure (CNKI), China Wanfang Database, and China VIP Database. Relevant references were tracked. This systematic database search aimed to comprehensively collect various research and academic literature to provide evidence and support for conceptual analysis. A total of 31 articles were included in the review.

Methods

This study employed Walker and Avant’s concept analysis method.

Results

A total of 31 papers were included in the study. The four primary characteristics of nurse AI anxiety are: Technical concerns, patient safety concerns, perceived ethical burden, and sense of occupational devaluation. The antecedents are categorized into individual, environmental and related to artificial intelligence. The consequences of AI anxiety are distinguished into individual-level and hospital-level impacts.

Conclusion

This study provides a comprehensive understanding of the concept of AI anxiety among nurses by outlining its antecedents, attributes, and consequences. The conceptualization of AI anxiety will facilitate future research aimed at establishing effective prevention strategies.