The Role of Artificial Intelligence in Nursing Care: An Umbrella Review

Abstract

Artificial intelligence (AI) is revolutionizing nursing by enhancing decision-making, patient monitoring, and efficiency. Machine learning, natural language processing (NLP), and predictive analytics claim to improve safety and automate tasks. However, a structured analysis of AI applications is necessary to ensure their effective implementation in nursing practice. This umbrella review aimed to synthesize existing systematic reviews on AI applications in nursing care, providing a comprehensive analysis of its benefits, challenges, and ethical implications. By consolidating findings from multiple sources, this review seeks to offer evidence-based insights to guide the effective and responsible integration of AI in nursing practice. A systematic umbrella review approach was employed following PRISMA guidelines. Multiple databases, including PubMed, CINAHL, Scopus, Web of Science, and IEEE Xplore, were searched for review articles published between 2015 and 2024. Findings were synthesized thematically to identify key trends, benefits, limitations, and research gaps. This review synthesized 13 studies, emphasizing AI's impact on clinical decision support, patient monitoring, nursing education, and workflow optimization. AI enhances early disease detection, minimizes diagnostic errors, and automates documentation, improving efficiency. However, data privacy risks, biases, ethical concerns, and limited AI literacy hinder integration. AI presents significant opportunities for improving nursing care, yet its successful implementation requires addressing ethical, legal, and practical challenges. Adequate AI training, robust data governance frameworks, and policies ensuring responsible AI use are essential for its integration into nursing practice. Future research should explore long-term AI impact, training models for nurses, and strategies to balance AI-driven efficiency with human-centered care.

Keywords: artificial intelligence in nursing; clinical decision support; ethical considerations in AI; nursing education; nursing workflow optimization; patient monitoring.