Nursing

An Interpretable Machine Learning Framework with Clinical Nomogram for Predicting In-Hospital Mortality in Acute Ischemic Stroke Using High-Granularity Bedside Data.

BACKGROUND: This multicenter study developed and validated an interpretable machine learning model integrating granular nursing and emergency department data collected within the first 24 hours to predict in-hospital mortality in acute ischemic stroke (AIS). METHODS: We analyzed a retrospective cohort of 5,014 adult AIS patients from three tertiary academic centers (2019-2023). Centers A and B (n=...

The Importance of Artificial Intelligence in Nursing: A Fundamentals of Care Perspective.

AIM: To analyze the integration of Artificial Intelligence in nursing through the lens of the Fundamentals of Care framework. DESIGN: A discursive paper. METHODS: This discursive paper synthesizes current literature and theoretical perspectives to examine the Relationship, Integration and Context dimensions of the Fundamentals of Care framework in the era of Artificial Intelligence. RESULTS: Artif...

Understanding the Functional Needs of Patients With Cardiovascular Disease Regarding Nursing Robots Using a Kano Survey Scale: Cross-Sectional Study.

BACKGROUND: With population aging and the growing burden of chronic diseases, the number of patients with cardiovascular disease (CVD) in China contin...

The Impact of Digital Transformation on Nursing Professional Experience: Critical Interpretive Synthesis Review Developing the Fragmented Professional Transition in Nursing (FPT-N) Model.

BACKGROUND: Digital transformation through electronic health records (EHRs), telehealth, mobile health, and emerging AI has reshaped nursing work. Bey...

The Imaging Compass: Navigating Reperfusion in Acute Ischemic Stroke.

The management of acute ischemic stroke has shifted from rigid time-based protocols to imaging-driven, tissue-based reperfusion strategies. Non-contra...

Understanding healthcare professionals' perceptions of artificial intelligence: A bibliometric and thematic analysis using VOSviewer.

BackgroundArtificial intelligence (AI) is transforming healthcare by influencing clinical decision-making, service delivery, and professional roles. H...

Attitudes, perceptions, and UTAUT-based factors influencing the acceptance of medical artificial intelligence among Chinese oncology healthcare professionals: a national cross-sectional survey.

OBJECTIVES: To conduct a nationwide survey among professionals working in oncology departments in China to investigate their attitudes, perceptions, a...

An AI-Facilitated Virtual Teaching Assistant for Undergraduate Nursing Honors Research: An Embedded Mixed-Methods Evaluation.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into higher education. However, evidence on theory-informed AI interventions suppo...

An Umbrella Review of Artificial Intelligence Applications in Mental Health Care.

BACKGROUND: Artificial intelligence (AI) is increasingly used in mental health care to address rising demand, workforce shortages and access barriers;...

Digital Health Competence and Attitudes Toward AI Among Health Care Professionals: Convergent Mixed Methods Study.

BACKGROUND: Digital transformation is reshaping health care systems and requires health care professionals to develop advanced digital health competen...

Advancing Nursing Practice Through Simulation: Enhancing Competency, Quality and Patient Outcomes.

Simulation has become an essential strategy for advancing nursing practice, clinical competence, patient safety and healthcare system performance. As ...

Small Intracerebral Hemorrhage: Baseline CT Edema-to-Hematoma Ratio as an Independent Predictor of Early Expansion.

BACKGROUND AND PURPOSE: Small intracerebral hemorrhage (ICH), defined as baseline NCCT hematoma volume (HV) <30 mL, is often considered lower risk for...

US Nursing Strikes Highlight Systemic Challenges: Can Digital Health Be Part of the Solution?

The first half of 2026 has seen several nursing strikes in the United States, driven by persistent workforce shortages and increasing workloads and bu...

From frailty-driven to frailty-informed care in the age of wearable AI.

As populations age, frailty is increasingly monitored through wearable technologies capable of continuously capturing behavioral and physiological cha...

Artificial intelligence-assisted phenotyping of sepsis: Research progress, clinical challenges, and translational prospects.

Sepsis remains a leading cause of mortality in intensive care units worldwide, a challenge exacerbated by pathophysiological and clinical heterogeneit...

AI Dependency and Brain Rot Among Nursing Students: A Mixed-Methods Study.

BACKGROUND: Artificial intelligence (AI) can be beneficial in tailoring learning experiences to each student's learning style, needs, and preferences....

Informatics Techniques in Nursing Course and Readiness for Medical Artificial Intelligence: The Mediating Role of Informatics Competence.

This descriptive and correlational study examined the relationship between completion of an Informatics Techniques in Nursing course and nursing stude...

Perceptions, knowledge and adoption of artificial intelligence in rheumatology: results from a British Society for Rheumatology survey.

OBJECTIVES: Artificial intelligence (AI) and machine learning applications are rapidly expanding across healthcare. Successful implementation of AI te...

Artificial intelligence and the phantom pneumothorax: Clinical reasoning still matters.

Artificial intelligence (AI) has become increasingly integrated into chest radiograph interpretation, demonstrating excellent diagnostic performance f...

Nurse Led Innovation: How Artificial Intelligence Streamlined Compliance Training and Saved Costs.

This improvement project reduced the cost and time associated with regulatory training through artificial intelligence (AI) and technology-enabled wor...