Latest AI and machine learning research in nursing for healthcare professionals.
BACKGROUND: We aimed to determine whether unsupervised machine learning was able to discover latent and possibly clinically-relevant clusters, hidden in dynamic electrical impedance tomography (EIT) images within a population of mechanically ventilated COVID-19 patients. Dynamic EIT images visualize the distribution of electrical impedance within the patient's lungs, and is clinically used for the...
BACKGROUND: The rapid integration of generative AI into scholarly writing has created an inconsistent policy landscape, challenging academic integrity in nursing education. PURPOSE: This study analyzed nursing journals' publishing policies on AI-generated content (AIGC) to create an evidence base for educators. METHODS: A cross-sectional analysis of nursing journals from the Scimago database was c...
INTRODUCTION: This paper explores the potential benefits and limitations of synthetic data (SD) in paediatrics, addressing the challenges of data scar...
PURPOSE: The number of adolescents and young adults (AYAs) with cancer has increased over the past 30 years. Fundamental to this process has been the ...
BACKGROUND: Effective pain management is a vital aspect of quality nursing care, requiring sound knowledge, assessment skills, clinical judgment, and ...
PURPOSE: The progression of acute kidney injury (AKI) to end-stage kidney disease (ESKD) poses challenges due to high risks of comorbidities and poor ...
Antibiotic discovery and antibiotic prescribing represent two domains that both stand to benefit from artificial intelligence (AI)-driven progress in ...
BACKGROUND: Advances in artificial intelligence (AI) tools are also necessitating a change in nursing education. AIM: This study evaluated the effect ...
BACKGROUND: Burnout, a global occupational health challenge, is particularly prevalent among Chinese nurses. Traditional research methods have limitat...
PURPOSE: Artificial Intelligence (AI) has the potential to enhance supportive care for cancer survivors from diagnosis through treatment and into surv...
AIM: To explore how first-year nursing students engaged with ChatGPT during an assessment task, and to understand the experiences, challenges, and per...
OBJECTIVES: To synthesize current educational approaches to AI literacy in oncology nursing, identify key competency domains along with barriers and e...
Cerebral blood flow (CBF) is under homeostatic control via cerebral autoregulation, maintaining a constant blood supply to brain parenchyma by integra...
BACKGROUND: Effective cancer symptom management significantly impacts patient outcomes and quality of life. While Clinical Decision Support Systems sh...
OBJECTIVES: This study aimed to explore pediatric oncology nurses' perspectives on the integration of artificial intelligence (AI) into pediatric onco...
BACKGROUND: Clinical decision-making is shaped by healthcare provider-related factors such as experience, qualification and cognitive skills. AI-based...
The rapid evolution of generative artificial intelligence (AI) is poised to transform medicine and medical education. Large language models (LLMs) hav...
We conducted a retrospective study to evaluate the performance of 5 large language models in detecting surgical site infections (SSIs), compared with ...
Cancer nanomedicine has evolved from the 1995 landmark approval of Doxil® into a programmable platform of precision oncology. The field now progresses...
OBJECTIVES: Electromyography (EMG) is increasingly applied in oncology to monitor neuromuscular impairment, treatment toxicities, and rehabilitation o...