Nurses' knowledge, attitudes, literacy, anxiety, and readiness towards artificial intelligence in clinical practice: An international scoping review.

Authors

Aanuoluwapo Clement David-Olawade, Adewoyin A Osonuga, David B Olawade

Published in

Applied nursing research : ANR. Volume 91. Pages 152135. Epub Aug 11, 2026.

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare delivery, yet its successful integration into clinical practice depends substantially on nurses, who form the largest group of frontline technology users.
This scoping review mapped and synthesized empirical evidence on nurses' knowledge, attitudes, literacy, anxiety, readiness, and perceived barriers regarding AI integration in nursing practice.
The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). PubMed, CINAHL, Scopus, and Web of Science were searched for empirical studies published between 2022 and 2026. Of 412 records identified, 33 studies (29 quantitative, one mixed methods, and three qualitative) involving more than 9000 nurses across 10 countries met the inclusion criteria.
Nurses' knowledge of AI was predominantly low to moderate, whereas attitudes were generally positive, with most nurses recognising AI's potential to reduce workload, support clinical decision-making, and improve the quality of patient care. AI literacy was moderate overall and consistently associated with lower AI-related anxiety. Anxiety and worry centred on job displacement, dehumanisation of care, accountability, and data security. Readiness and behavioural intention were shaped by attitudes, digital literacy, ethical awareness, trust, prior AI training, age, and gender. Barriers included inadequate training, limited infrastructure, and absent governance frameworks.
Nurses display guarded optimism towards AI, but substantial knowledge and preparedness gaps persist across regions. Because the evidence rests largely on observational studies of varying methodological quality, positive attitudes should not be read as implementation readiness; structured AI education, clear ethical governance, and sustained organisational support are needed to translate favourable attitudes into safe clinical adoption.

PMID:
42785821
Bibliographic data and abstract were imported from PubMed on 25 Sep 2026.

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