Integrative Review of Artificial Intelligence Applications in Nursing: Education, Clinical Practice, Workload Management, and Professional Perceptions

Rabie Adel ElArab¹Omayma Abdulaziz¹Mette Sagbakken^2*Ahmed Ghannam³Fuad Abuadas⁴Joel G Somerville⁵Abbas Al Mutair¹

• ¹Almoosa College of Health Sciences, Al Ahsaa, Saudi Arabia
• ²Oslo Metropolitan University, Oslo, Norway
• ³Princess Sumaya University for Technology, Amman, Amman, Jordan
• ⁴College of Nursing, Al Jouf University, Sakaka Aljouf, Saudi Arabia
• ⁵University of the Highlands and Islands, Inverness, Scotland, United Kingdom

Background: Artificial Intelligence (AI) is rapidly transforming the nursing profession, presenting significant opportunities and challenges..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. Results: This review encompassed 25 studies, from which six overarching themes emerged. Education & 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 & 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 & 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 & 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. Nursing Perceptions: Across practice settings, 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. This framework offers a structured, ethically informed, and user-centric approach, advocating for AI as complementary to human expertise planning that addresses educational, clinical, ethical, organizational, participatory, and economic dimensions, reinforcing the core humanistic values of nursing.