Factors influencing registered nurses’ attitudes toward and literacy in artificial intelligence: a cross-sectional systematic review
Year: 2026
Type: review
Study design: Systematic Review
Abstract: Background Presently, artificial intelligence technology is being extensively applied across clinical nursing practice. However, whether this technology can be deeply integrated into clinical workflows depends largely on nurses’ attitudes and literacy regarding artificial intelligence. Consequently, conducting an in depth exploration of core factors shaping registered nurses’ artificial intelligence attitudes and literacy holds significant importance for driving the rapid development of intelligent nursing. Methods This study strictly adhered to PRISMA guidelines. Systematic searches were conducted across seven English databases, namely PubMed, Web of Science, Medline, Embase, The Cochrane Library, IEEE Xplore, and CINAHL, alongside three Chinese databases, including China National Knowledge Infrastructure (CNKI), Wanfang Data, and VIP Database. The search timeframe ranged from database inception through May 30, 2026. Following screening according to inclusion and exclusion criteria, independent factors influencing attitudes and literacy were extracted. Employing a narrative synthesis approach, these influencing factors were categorized, summarized, and narratively integrated across distinct thematic domains. Results A total of 30 studies involving 12,958 registered nurses were included in this systematic review. The majority of the studies were conducted in China and Turkey. The independent factors influencing registered nurses’ attitudes toward artificial intelligence included educational attainment, age, length of service, gender, professional title/nursing level, hospital tier/category, AI awareness/understanding/knowledge, AI training experience, AI usage experience, artificial intelligence anxiety, and digital leadership. The factors independently affecting nurses’ artificial intelligence literacy included leadership style, AI training experience, and usage experience/frequency. Conclusion Current evidence indicates that key modifiable factors to improve nurses’ attitudes toward and literacy in artificial intelligence include AI-related training, practical hands-on experience, and organizational leadership, while technical anxiety should not be overlooked. Compared with demographic characteristics, educational and experiential interventions appear more effective in enhancing nurses’ artificial intelligence literacy levels. Nursing managers should actively implement smart training programs, increase opportunities for nurses to apply new technologies in clinical practice, and formulate relevant policies regarding data privacy protection and accountability division, thereby promoting the safe and effective integration of AI technology into patient care. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/recorddashboard
Source: Frontiers in Public Health
Authors: Yingqi Dai, Xili Zhao, Xiao Pan, Jiaxin Wei
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7223347169