Latest AI and machine learning research in emergency medicine for healthcare professionals.
Pregnant women and children have been underrepresented in clinical studies due to ethical concerns and perceived vulnerabilities. This resulted in a significant gap in knowledge regarding the safety and efficacy of medications for these populations. Maternal and Pediatric PRecision In Therapeutics Knowledge Portal (MPRINT-KP) is designed to provide a comprehensive view of pharmacokinetic, pharmaco...
PURPOSE: DirectDensity enables tube voltage-independent, density-calibrated computed tomography (CT) images for treatment planning and is therefore increasingly used in radiotherapy facilities. In accelerated emergency workflows, diagnostic CT (dCT) data are often acquired without DirectDensity. It remains unclear whether using a DirectDensity calibration curve in this context leads to major dose ...
Sudden arrhythmic death syndrome (SADS) is a major cause of sudden cardiac death in young individuals, characterized by structurally normal hearts and...
AIM: To evaluate the value of machine learning in assessing intraoperative blood loss by comparing associated outcomes with those of the gold standard...
Visual point-of-care testing (POCT) technologies convert biomolecular events into naked-eye readable signals. These systems offer rapid assay times, u...
Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a major cause of severe thrombocytopenia, intracranial hemorrhage, and long-term neurologica...
Acetaminophen (APAP) overdose is a leading cause of drug-induced liver injury and acute liver failure. PANoptosis, a recently defined form of programm...
BACKGROUND: Suicide and self-harm are significant issues globally. Accurate, efficient and comprehensive data are required to identify people who pres...
Ore particle size distribution is an important metric for evaluating blasting outcomes and affects the energy consumption of ore crushing equipment. F...
With the rising global incidence of infertility and the growing proportion of infants conceived via in vitro fertilization-embryo transfer (IVF-ET), m...
Artificial intelligence (AI) has the potential to transform how drug development and clinical trials are conducted. The 2025 Infectious Disease Clinic...
OBJECTIVE: To develop and evaluate an internally validated natural language processing (NLP) model to determine guideline adherence of antibiotic deci...
Animal models are crucial in biomedical research, particularly in pharmaceutical discovery and safety testing. Recent legislative updates and regulato...
Artificial intelligence (AI) is transforming toxicology by enabling faster, more accurate, and more equitable approaches to diagnosis, treatment, rese...
Predicting the atmospheric dispersion of radionuclides is central to nuclear emergency response, yet any useful prediction tool must balance physical ...
OBJECTIVES: To develop and evaluate a machine learning (ML) model that predicts Crohn's disease (CD) patients responsible for the top quartile of heal...
BACKGROUND AND PURPOSE: Artificial intelligence (AI) models have shown promise in neuroradiology, yet their real-world generalizability remains uncert...
Enhancing vehicle emergency braking performance is crucial for vehicular safety and reliability. We have observed that the traditional vehicle dynamic...
The post-tectonic granites and pegmatites of the Abu Rusheid-Sikait area, in the South Eastern Desert of Egypt, represent highly mineralized plutons w...
Artificial intelligence holds transformative potential for clinical triage, yet challenges in accuracy, generalization, and interpretability persist. ...