Latest AI and machine learning research in nursing for healthcare professionals.
This study reports the first steps toward establishing a computer vision system to help caregivers of bedridden patients detect pressure ulcers (PUs) early. While many previous studies have focused on using convolutional neural networks (CNNs) to elevate stages, hardware constraints have presented challenges related to model training and overreliance on medical opinions. This study aimed to develo...
Normative mapping is a framework used to map population-level features of health-related variables. It is widely used in neuroscience research, but the literature lacks established protocols in modalities that do not support healthy control measurements, such as intracranial EEG (icEEG). An icEEG normative map would allow researchers to learn about population-level brain activity and enable comp...
STUDY OBJECTIVES: Despite frequent sleep disruption in the pediatric intensive care unit, bedside sleep monitoring in real time is currently not avail...
BACKGROUND: Mild cognitive impairment and early-stage dementia significantly impact healthcare utilization and costs, yet more than half of affected p...
AIM: The purpose of this study is to compare the efficacy of an artificial intelligence (AI)-based care plan learning strategy with standard training ...
AIMS: This study aims of determine the mediating role of individual innovativeness in the effect of nursing students' artificial intelligence anxiety ...
OBJECTIVE: This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriatene...
Artificial intelligence (AI) has been increasingly used in delivering mental healthcare worldwide. Within this context, the traditional role of mental...
BACKGROUND: Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols an...
BACKGROUND: High-stress environments, heavy workloads, and the emotional demands of patient care, which are common challenges faced by nurses, are fac...
BACKGROUND: This study aims to develop an active following technology of the mirror-holding arm of a bedside intelligent surgical robot that enables r...
OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based...
To ensure the quality of care for inpatients in ophthalmic hospitals, address the complex and variable conditions of postoperative patients, and condu...
Chronic wounds affect 8.5 million Americans, particularly the elderly and patients with diabetes. These wounds can take up to nine months to heal, m...
BACKGROUND: Utilizing Artificial Intelligence (AI) in clinical settings may offer significant benefits. A roadblock to the responsible implementation ...
The recent widespread adoption of drones for studying marine animals provides opportunities for deriving biological information from aerial imagery....
This work proposes a computer vision framework to automate the extraction of vital signs from bedside monitor systems and facilitate adaptive drug inf...
Late-stage failures of monoclonal antibody (mAb) programs often reflect developability liabilities, including high-concentration viscosity and rapid c...
Assessing the degree and characteristics of consciousness is central to caring for patients with Disorders of Consciousness (DoC), yet current standar...
Large language models (LLMs), such as GPT-4, are increasingly integrated into healthcare to support clinicians in making informed decisions. Given Cha...