Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions

Background: Artificial Intelligence (AI) is rapidly transforming the nursing profession, presenting significant opportunities and challenges. Despite its promising potential in enhancing nursing education, clinical practice, and operational efficiency, critical barriers related to ethics, workforce adaptation, and humanistic care persist. Aim: This integrative review systematically evaluates the integration of AI in nursing practice, with a specific focus on nursing education, clinical care, workload management, and professional perceptions. Methods: Guided by PRISMA 2020 and the SPIDER framework, a thematic synthesis was conducted. Study quality was assessed using the Mixed Methods Appraisal Tool (MMAT), and the risk of bias evaluated through ROBINS-I. Results: This review encompassed 25 studies, from which six overarching themes emerged. Education and training: AI-powered simulations and content-creation platforms enriched nursing curricula by presenting realistic clinical scenarios, which consistently yielded deeper student engagement, enhanced case-management performance, and higher satisfaction scores. Learners also reported an increased cognitive load and heightened stress levels when navigating these more complex, AI-driven activities. Clinical decision support and monitoring: AI-enabled alert algorithms and wearable sensors enabled nurses to detect subtle signs of patient deterioration and fever significantly earlier than conventional methods, supporting timelier clinical interventions. Qualitative feedback from critical-care staff underscores that these automated insights must be balanced with professional judgment to avoid overreliance. Rehabilitation and postoperative care: In neurosurgical, gynecological, and orthopaedic settings, AI-guided imaging tools and personalized follow-up pathways were linked to smoother recovery trajectories, streamlined follow-up processes and richer patient feedback, and exceptionally high patient satisfaction. Nurses noted that these technologies enhanced the precision of assessments without wholly replacing the need for human touch. Workload and workflow management: AI systems that automated routine follow-up tasks and generated predictive workload models freed nurses from repetitive, non-clinical duties and offered data-driven insights to inform staffing decisions. These efficiencies allowed nursing teams to devote more time to direct patient care and were associated with reductions in burnout and improved workplace morale. Nursing perceptions: Across practice settings, nursing students and practicing nurses broadly welcomed AI’s ability to streamline workflows and support decision-making, recognizing its potential to elevate patient care and professional practice. Ethical implications: Simultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment. Framework and recommendations: The Nursing AI Integration Roadmap (NAIIR) was developed, emphasizing transformational education, advanced clinical integration, ethical governance, robust organizational infrastructure, participatory design, and rigorous economic evaluation. This framework offers a structured, ethically informed, and user-centric approach, advocating for AI as complementary to human expertise. Conclusion: Successfully integrating AI into nursing requires comprehensive strategic planning that addresses educational, clinical, ethical, organizational, participatory, and economic dimensions, reinforcing the core humanistic values of nursing. Of the 25 included studies, 21 were judged at moderate risk of bias; despite this limitation, evidence suggests improvements in critical thinking, learner engagement, and clinical satisfaction across diverse educational and practice settings.