Latest AI and machine learning research in nursing for healthcare professionals.
Resource Constrained Situations (RCS) at Emergency Medical Dispatch centers where there are more patients requiring an ambulance than there are available ambulances are common. Machine learning (ML) techniques offer a promising but largely untested approach to assessing relative risks among these patients. The study aims to establish whether the provision of ML-based risk scores predicting patient...
Real-world adoption of closed-loop insulin delivery systems (CLIDS) in type 1 diabetes remains low, driven not by technical failure, but by diverse behavioral, psychosocial, and social barriers. We introduce ChatCLIDS, the first bench-mark to rigorously evaluate LLM–driven persuasive dialogue for health behavior change. Our framework features a library of expert-validated virtual patients, each wi...
Early-onset sepsis (EOS), which occurs within the first 72 hours of life, can often be fatal for neonates. Machine learning (ML) models demonstrate pr...
Aspiration causes or aggravates a variety of respiratory diseases. Subjective bedside evaluations of aspiration are limited by poor inter-and intra-ra...
Large language models (LLMs) show promise on healthcare tasks, yet most evaluations emphasize multiple-choice accuracy rather than open-ended reasonin...
Hospital readmissions represent a persistent challenge for healthcare systems, often stemming from inadequate post-discharge monitoring. This study pr...
Clinicians currently lack reliable tools to determine, at the point of mild cognitive impairment (MCI), which individuals will progress to Alzheimer’s...
Large language models (LLMs) demonstrate strong performance on medical reasoning tasks, but current evaluation approaches focus primarily on accuracy,...
Point-of-care ultrasonography (POCUS) enables clinicians to obtain critical diagnostic information at the bedside especially in resource limited setti...
Manual inpatient screening for substance misuse is labor-intensive and inconsistently applied. Evaluation of artificial intelligence (AI)–assisted scr...
Accurate early prediction of neurological outcomes in comatose patients after cardiac arrest is critical for guiding therapeutic decisions and improvi...
Lower respiratory tract infection (LRTI) is a leading cause of morbidity and mortality among children admitted to paediatric intensive care units (PIC...
Ongoing education in HIV care is limited for many healthcare providers working in rural and non-academic settings, which can reduce patients’ access t...
Accurate prediction of neurological outcome after cardiac arrest is essential for guiding intensive care decisions. Electroencephalography (EEG) suppo...
Acute appendicitis is a common but diagnostically challenging surgical emergency in children. Existing linear scoring systems lack sufficient accuracy...
This study aimed to systematically review and critically evaluate the risk of bias and applicability of surgical site infection (SSI) risk prediction ...
Machine learning models hold promise in cancer medicine but often lack robustness and interpretability. We introduce a new class of model for high-dim...
BACKGROUND: Artificial intelligence (AI) is significantly transforming the nursing profession, enhancing patient care, and shaping future nursing prac...
AIM: This study aims to use bibliometric methods to analyze highly cited nursing education articles on digital health, particularly those 100 top cite...
Monitoring the remaining food in patients' trays is a routine activity in healthcare facilities as it provides valuable insights into the patients' di...