Emergency Medicine

Artificial Intelligence Technologies Supporting Nurses' Clinical Decision-Making: A Systematic Review.

BACKGROUND: The use of technology to support nurses' decision-making is increasing in response to growing healthcare demands. AI, a global trend, holds great potential to enhance nurses' daily work if implemented systematically, paving the way for a promising future in healthcare. OBJECTIVES: To identify and describe AI technologies for nurses' clinical decision-making in healthcare settings. DESI...

Hereditary syndromes and RCC: what radiologists need to know.

Hereditary renal cell carcinoma (RCC) accounts for approximately 5-8% of all renal cancers. This review provides a comprehensive overview of the seven hereditary RCC syndromes recognized by the National Comprehensive Cancer Network: Tuberous Sclerosis Complex (TSC), Von Hippel-Lindau (VHL), Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC), Hereditary Papillary Renal Carcinoma (HPRC), Birt-H...

Artificial intelligence‑based quantitative analysis of hepatic fibrosis in carbon tetrachloride-induced mouse model of metabolic dysfunction-associated steatohepatitis.

UNLABELLED: Liver fibrosis, a major histopathological indicator of chronic liver injury, is also a key feature of metabolic dysfunction-associated ste...

Reveal a risk and potential mechanism for keratoconus progression with isotretinoin: Toxicogenomic, transcriptomics and real-world evidence insights.

PURPOSE: Limited clinical data and unstable animal models hinder understanding isotretinoin-induced keratoconus. We addressed this by integrating phar...

Artificial intelligence for mortality risk stratification in septic shock: A systematic review and meta-analysis.

BACKGROUND: Septic shock is associated with high mortality, making accurate risk stratification crucial for tailored treatment. Traditional clinical s...

Machine learning identifies traumatic experiences as being associated with auditory verbal hallucinations in both a non-clinical population and individuals diagnosed with psychosis.

BACKGROUND: Auditory verbal hallucinations (AVHs) are a core symptom of psychosis but their prevalence in the general population ranges from 5-28 %. M...

Interpretable machine learning framework for predicting pesticide phytotoxicity in wastewater reuse: Integrating molecular, quantum, and experimental descriptors.

Pesticides are essential for crop protection, but their potential toxicity poses significant environmental and health risks. Although numerous toxicol...

Diagnostic performance of X-ray-based deep learning models for detecting ankle and foot fractures: a systematic review and meta-analysis.

OBJECTIVES: Lower extremity fractures are prevalent in vulnerable populations leading to significant burdens, which highlight the need for timely and ...

An explainable machine learning model predicts 30-day readmission after vertebral augmentation.

Osteoporotic vertebral compression fracture (OVCF) patients face high 30-day readmission risks after vertebral augmentation procedures (VAPs). Using e...

From passive prevention to proactive intervention: An AI-assisted integrated governance system for algal bloom monitoring and control.

Algal blooms, characterized by the excessive proliferation of microalgae in a freshwater or marine ecosystem, have evolved into a global ecological, h...

Ambient Artificial Intelligence Versus Human Scribes in the Emergency Department.

STUDY OBJECTIVE: To compare the use of ambient artificial intelligence (AI) versus human scribes in the emergency department in terms of note quality ...

Machine learning models for predicting hospital admission in pediatric emergency departments: A systematic review.

CONTEXT: Pediatric Emergency Departments (PEDs) face overcrowding partially due to delayed hospital admission decision. Machine Learning (ML) models c...

The effect of robot-assisted training on burn rehabilitation: A systematic review and meta-analysis.

OBJECTIVE: This study aimed to systematically evaluate the efficacy of robot-assisted training (RAT) in improving the physical rehabilitation outcomes...

Advancements in burn size assessment: A systematic review of emerging technologies.

BACKGROUND: Accurate and reproducible assessment of total body surface area (TBSA) in burns is vital for guiding fluid resuscitation, triage, referral...

Ultrasound to guide critical decisions: What you need to know.

BACKGROUND: Hypotension in the intensive care unit (ICU) demands rapid diagnosis and intervention, as delays in identifying the etiology of shock dire...

An explainable machine learning model for comorbidity risk stratification in patients with fractures admitted to the intensive care unit: a multicenter study.

BACKGROUND: Among traumatic-fracture patients admitted to intensive care units (ICUs), those with substantial chronic comorbidities recover more slowl...

Removal of a quaternary ammonium compound by electrocoagulation: Mechanistic analysis and multi-response optimization using response surface methodology and machine learning.

The widespread use of quaternary ammonium compounds (QACs), intensified by the COVID-19 pandemic, has led to their increasing presence in aquatic envi...

Patients visits forecasting in emergency departments: Self-adapting LSTM models for evolving data distributions.

BACKGROUND AND OBJECTIVE: Overcrowding in emergency departments (EDs) remains a critical challenge in healthcare systems. Accurate forecasting of pati...

Deep learning framework for bronchoscopic diagnosis of burn inhalation injury.

Burn inhalation injury (BII) increases mortality and morbidity in burns patients. Accurate bronchoscopic grading, as the gold standard diagnostic moda...

Digital Eye: Deep Learning for Detecting Physeal Fractures of the Pediatric Distal Radius.

BACKGROUND: Distal radial fractures (DRFs) are some of the most common pediatric injuries, often involving the physis. Diagnostic accuracy can be chal...