Latest AI and machine learning research in nursing for healthcare professionals.
Nursing professional development practitioners manage complex systems supporting onboarding, competency validation, continuing education, and regulatory compliance. Paper-based workflows create inefficiencies and increase administrative burden. Digital technologies and responsible artificial intelligence can modernize practice, improve engagement, and enhance documentation accuracy. This article d...
ANCC-accredited nursing continuing professional development providers must evaluate activities and aggregate outcomes to demonstrate mission alignment, learner impact, and program effectiveness. Open-ended evaluations offer valuable insights but are often underused because manual analysis is time-consuming and inconsistent. This practice innovation uses generative artificial intelligence as a deci...
OBJECTIVE: To develop and compare three machine learning models for identifying factors associated with workplace violence (WPV) among emergency depar...
OBJECTIVE: This study explored how artificial intelligence (AI) technologies support the nondiagnosing/nontreatment roles of allied health workers, ex...
BACKGROUND: AI has shown significant potential in intensive care unit (ICU) nursing practice, enhancing efficiency, decision-making, and patient safet...
BACKGROUND: Enhancing nursing students' awareness, attitudes, beliefs, and preparedness toward AI may help improve their health care knowledge and pra...
Cardiovascular risk prediction from heterogeneous physiological signals supports early warning in bedside and wearable monitoring, where ECG, PPG, HRV...
BACKGROUND: Artificial intelligence (AI) in health care is typically evaluated on efficiency, predictive accuracy, and clinical decision support. Far ...
For more than three decades, pharmacy has narrated its evolution as a movement away from the product and toward the patient. The pharmaceutical care e...
OBJECTIVE: Sepsis is a leading cause of mortality in low- and middle-income countries. This study identifies computable pediatric sepsis phenotypes (P...
Advanced haemodynamic monitoring of cardiac index is restricted to selected high-risk cases, leaving most patients undergoing major surgery without re...
The aim of this study is to evaluate the effect of an artificial intelligence (AI)-powered virtual patient application (ChatGPT) on the development of...
OBJECTIVES: Advanced ovarian cancer survivorship requires multidisciplinary coordination. As patients use large language models (LLMs) as clinical nav...
BACKGROUND: The rapid uptake of generative artificial intelligence (GenAI) in higher education has increased both enthusiasm and concern. While studen...
AIM: To explore the notion of post-humanism and the impact of artificial intelligence (AI) on society, nursing and healthcare. DESIGN: Discursive pape...
AIM: To examine nursing academics' perceptions and experiences of artificial intelligence (AI) integration in nursing education. DESIGN: Scoping revie...
BACKGROUND: Successful implementation of artificial intelligence (AI) in healthcare depends not only on technological performance but also on the read...
The global burden of cancer is significant, with millions of new diagnoses expected annually, necessitating highly coordinated and patient-centered ca...
The integration of artificial intelligence and robotics into clinical medicine is no longer a question of whether but of how, and physicians currently...
Nurse managers spend a significant amount of their working day on administrative duties, notably rostering, which can leave them with little time to e...