Latest AI and machine learning research in nursing for healthcare professionals.
PURPOSE OF REVIEW: To provide a practical, physiology-driven framework for advanced hemodynamic monitoring in patients with acute heart failure (AHF) and cardiogenic shock, highlighting and supporting clinicians in integrating monitoring tools into an individualized bedside approach. RECENT FINDINGS: Cardiogenic shock is increasingly recognized as a heterogeneous syndrome involving not only impair...
Workplace violence and sleep disorders are common among emergency department (ED) nurses, yet heterogeneity in violence exposure and its relationship with sleep disorders remains poorly understood. This nationwide cross-sectional study included 1540 Chinese ED nurses. Latent profile analysis identified violence exposure patterns, while LASSO, 7 machine learning algorithms, and SHAP were used for s...
BACKGROUND: Generative artificial intelligence (AI) may support systematic review learning, but general-purpose chatbots and workflow tools do not exp...
BACKGROUND: Machine learning (ML) has been demonstrated to enhance health care cost prediction by handling high-dimensional data and identifying compl...
OBJECTIVES: This scoping review aimed to map the available evidence on digital predictive technologies for fall risk assessment, prediction, and preve...
BACKGROUND: Injuries are responsible for 950,000 deaths per year among children and adolescents under 18Â years old. Trauma prediction scores are usefu...
Threats of workforce displacement and ethical uncertainty accompany the use of artificial intelligence in palliative care and nursing in general. Dimi...
Artificial intelligence (AI) is increasingly influencing nursing practice and care delivery. This cross-sectional correlational study examined nurses'...
Postoperative delirium (POD) is a common perioperative complication involving central nervous system dysfunction, particularly among critically ill an...
PURPOSE: Ambulatory clinics manage high-cost medications with little visibility into quantity or movement, leaving unrealized opportunities for invent...
BACKGROUND: Large language models (LLMs) have shown promising performance on medical examinations across specialties. However, comparative evaluations...
AimTo gather nurses' perceptions nursing hematologic management on patients undergoing CAR-T cell therapy. A combined approach using statistical analy...
Artificial intelligence (AI) responsiveness in nursing was conceptually analyzed to clarify its defining attributes, antecedents, consequences, and em...
Artificial intelligence (AI) has become an increasingly prominent force in medicine, driven by rapid technical advances and a growing number of clinic...
This scoping review follows the Arksey and O'Malley five-stage framework: (1) identifying research questions, (2) identifying relevant studies, (3) se...
This study aimed to translate and culturally adapt the Medical Artificial Intelligence Readiness Scale for Medical Students into Korean and to examine...
BACKGROUND: Artificial intelligence (AI) is reshaping clinical decision support systems (CDSSs). In acute and critical care, nurses provide continuous...
BACKGROUND: Artificial intelligence-assisted early warning systems (AI-EWS) are increasingly integrated into critical care, yet little is known about ...