The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping...

Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in healthcare, with significant potential to enhance nursing practice, particularly in intensive care units (ICUs). ICUs pose complex challenges, including high patient acuity, ICU delirium, and nurse workload. These factors demand innovative technological solutions.

This scoping review comprehensively explores the current picture of AI and ML applications in critical care nursing, focusing on decision support systems, predictive analytics, workflow automation, and patient engagement tools.

A search of Four databases (Scopus, PubMed/MEDLINE, Science Direct, and CINAHL) was conducted for original peer-reviewed studies published between January 2019 and September 2025. The 2019 start date was selected to capture the contemporary wave of AI applications in critical care nursing, coinciding with the documented exponential growth in AI-related ICU publications following widespread EHR adoption and the maturation of deep learning architectures.

Five key themes were identified: predictive analytics and early warning systems, clinical decision-support tools, automation and workflow enhancements, monitoring combined with human-AI collaboration, and implementation challenges. Findings reveal that AI can reduce administrative burden and improve care quality. However, significant gaps persist, especially in evaluating long-term outcomes, nurse involvement, and ethical implementation.

This scoping review provides a contemporary, integrated thematic synthesis of machine learning and AI applications in critical care nursing. While not claiming absolute novelty, this review addresses a distinct and timely gap by simultaneously mapping predictive analytics, clinical decision support, workflow automation, and implementation challenges within a single evidence synthesis.

AI and machine learning may support critical care nurses by facilitating earlier recognition of patient deterioration, strengthening clinical decision-making, and reducing repetitive workload. Successful implementation requires nurse involvement in system design, appropriate training, transparent algorithms, and integration with existing clinical workflows.