Nursing

Perception of AI Symptom Models in Oncology Nursing: Mixed Methods Evaluation Study.

BACKGROUND: Patients undergoing cancer treatment experience a significant symptom burden. The standard process of symptom management includes patient reporting and clinical response following symptom escalation. Emerging predictive symptom models use artificial intelligence (AI) components of machine learning and deep learning to identify the risk of symptom deterioration, facilitating earlier int...

Pathophysiological mechanisms of fatigue and multidisciplinary management strategies (Review).

Fatigue is a common clinical symptom, and its complex pathophysiological mechanisms markedly affect the quality of life and social function of patients. With the advancement of omics technologies and artificial intelligence applications, the ability to understand the mechanisms of fatigue has been notably enhanced. Fatigue is a complex process involving the interaction of multiple systems and fact...

Development and Validation of a Machine Learning Model That Uses Voice to Predict Aspiration Risk.

BACKGROUND: Aspiration causes or aggravates a variety of respiratory diseases. Subjective bedside evaluations of aspiration are limited by poor inter-...

An Explanation User Interface for Artificial Intelligence-Supported Mechanical Ventilation Optimization for Clinicians: User-Centered Design and Formative Usability Study.

BACKGROUND: The integration of artificial intelligence (AI) into clinical decision support systems (CDSSs) for mechanical ventilation in intensive car...

Detection of Swallowing Abnormalities in Pediatric FEES Recordings Using Rule-Based and Model-Based Methods.

Pediatric swallowing dysfunction (SwD) poses serious health risks, including aspiration, malnutrition, and recurrent respiratory infections, making ea...

AI Agent for Delirium Screening among Patients in Oncology and Cardiac Intensive Care Units: A Proof-of-Concept Study.

AIM: To develop and evaluate an autonomous artificial intelligence (AI) agent to support nurse-led delirium screening and guideline-concordant prevent...

Development and Assessment of a Custom GPT-4o Chatbot for Postoperative Counseling and Frequently Asked Questions in Breast Surgery Patients.

PURPOSE: Chatbots have an impressive ability to answer patient inquiries and assist medical personnel, but they are limited by specialty-specific know...

Global priorities for advancing cancer survivorship.

PURPOSE OF REVIEW: Cancer survivorship is increasingly recognized as an important component of cancer care, yet access to high-quality care remains in...

Development of a Machine-Learning Model for Predicting Postoperative Complication Occurrence After Radical Gastrectomy Using Electronic Medical Records.

This study aims to develop a machine-learning model using electronic medical records to predict postoperative complications after radical gastrectomy ...

Risk Factors for Postoperative Delirium in Nonintensive Care Unit Patients: Machine Learning Approach.

Postoperative delirium is a frequent and serious complication lacking effective prediction tools for general ward patients. This study aimed to identi...

Transforming clinical toxicology in Singapore: four decades of progress and beyond.

By examining key milestones, challenges and future directions, this review chronicles the evolution of clinical toxicology in Singapore into a recogni...

Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.

BACKGROUND: Mild cognitive impairment and early dementia (MCI-ED) are frequently unrecognized in routine care, particularly in home health care (HHC),...

The Importance of Artificial Intelligence Literacy in Nursing and Midwifery to Achieve the Sustainable Development Goals: Perspectives From the 80th Session of the United Nations General Assembly.

The Effectiveness of Artificial Intelligence-Enhanced Interventions for Cancer Patients: A Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: The incidence of cancer continues to increase, and cancer patients still suffer from a range of burdens, leading to decreased quality of l...

Generative AI adaptive narratives to enhance nursing diagnostic reasoning: a classroom innovation.

Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer: A Scoping Review.

BACKGROUND: Artificial intelligence, particularly machine learning, has great potential to improve health outcomes, including predicting adverse condi...

Constructing a comprehensive nursing risk assessment model for older adults inpatients with multiple chronic conditions: A multicenter cross-sectional study.

AIM: To build a comprehensive nursing risk model for older adults inpatients with multiple chronic conditions to identify nursing risks. METHOD: This ...

Subphenotypes in acute respiratory distress syndrome: A scoping review across clinical, biological, computational, imaging, omics, and artificial intelligence approaches.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a heterogeneous syndrome with high mortality. Subphenotyping may identify more homogeneous g...

Generative AI in simulation debriefings: an exploratory study using the Team-FIRST framework and qualitative feedback from simulation experts and learners.

BACKGROUND: Effective debriefings in simulation-based education require accurate observation of team interactions, yet facilitators face challenges du...

Attitudes toward artificial intelligence and impostor phenomenon among nursing students: A five-country cross-sectional study.

BACKGROUND: AI tools are increasingly visible in nursing education and practice, yet student exposure and acceptance vary across settings. Limited dig...