Latest AI and machine learning research in prevention of medical errors for healthcare professionals.
AIMS: To evaluate the acceptability and feasibility among nurses of Decubitus Risk Prediction Alerts based on Artificial Intelligence (DRAAI), and to assess the feasibility of the implementation plan. DESIGN: A process evaluation of a pilot implementation study using mixed methods. METHODS: Acceptability and feasibility of DRAAI among nurses from three general wards in a university hospital was as...
PURPOSE OF REVIEW: Recent literature describes the deployment of different artificial intelligence (AI) technologies to potentially support infection prevention and control (IP&C) in both the community and healthcare environment. However, most studies focus on adults. This review explores the data and potential for AI to enhance IP&C for pediatric populations as well as recognizing important limit...
Diffusion-based mobile molecular communication (MMC) systems have shown great potential in nanoscale communication, particularly in the scenarios invo...
Vascular Cognitive Impairment and Dementia (VCID), the second most common form of dementia, is becoming increasingly prevalent worldwide. However, cur...
OBJECTIVE: To examine the influence of the emerging use of generative artificial intelligence (GenAI) within electronic health records and among the p...
BACKGROUND: Accurate individual risk assessment is crucial for guiding and improving the prevention of atherosclerotic cardiovascular disease (ASCVD)....
BACKGROUND: Incisional hernia (IH) is a significant complication that occurs after midline laparotomy and is associated with high morbidity and econom...
Cervical spondylotic myelopathy (CSM) and parkinsonian syndromes (PS) present similar motor symptoms, often causing misdiagnosis due to current clinic...
BACKGROUND: The use of artificial intelligence (AI) to analyze health care data has become common in behavioral health sciences. However, the lack of ...
Intrusion detection systems (IDS) leveraging federated learning (FL) are increasingly deployed in Internet of Things (IoT) environments to address dis...
BACKGROUND: Dementia and Parkinson's disease (PD) are among the most prevalent neurological disorders globally. Most previous research has focused on ...
OBJECTIVE: To reveal the intellectual framework, research trends, and gaps, and evaluate effective health literacy tools in the field of primary healt...
UNLABELLED: Childhood obesity is the main driver of early metabolic risk, predisposing to cardiovascular disease (CVD) and type 2 diabetes (T2D), whic...
Artificial intelligence and machine learning (AI/ML) in prevention science may improve or perpetuate health inequities. Community engagement is one pr...
Deep learning has the potential to address the bottleneck of conventional medical microwave tomography, which is ill-posed and has a high computation ...
BACKGROUND: Acute appendicitis poses diagnostic challenges due to symptom overlap with other abdominal conditions, often leading to misdiagnosis or mi...
BACKGROUND: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, arising from complex interactions among demographic, clini...
Communication is considered as a crucial approach for solving complicated multi-agent reinforcement learning (MARL) cooperative tasks. However, existi...
Effective patient-physician communication is a cornerstone of surgical care, yet increasing clinical complexity and time constraints often limit oppor...
We aim to present recent advancements in predictive markers for lymphomagenesis in SjD, concisely organize existing knowledge, and identify correspond...