Latest AI and machine learning research in emergency medicine for healthcare professionals.
The diagnostic accuracy of AIDOC-VO, the first commercial artificial intelligence tool for intracranial large-and medium-vessel occlusion (LVO/MeVO) detection on head-and-neck CT angiography (CTA), was evaluated in a multicenter emergency setting. A prospective diagnostic-accuracy study of 3,031 adult CT angiograms (mean age, 67.3 years ± 16.4 [SD]; 1,549 females) acquired March-July 2024 across a...
The development of new approach methodologies (NAMs) is increasingly enabling the replacement and reduction of animal use in research, complementing the ethical framework provided by the 3Rs (replacement, refinement, and reduction) of animal research. The European Commission has supported these approaches by embedding 3R requirements in policy frameworks and providing dedicated funding for 3Rs/NAM...
AIMS: To develop, deploy and evaluate artificial intelligence (AI) for triaging duodenal biopsies within a National Health Service (NHS) histopatholog...
Large-core anterior circulation ischemic stroke (LCIS) complicated by malignant cerebral edema (MCE) remains a leading cause of early death and profou...
Wound healing is a dynamic and highly coordinated biological process involving hemostasis, inflammation, proliferation, and tissue remodeling. However...
BACKGROUND: Conflict zones severely disrupt healthcare access, with millions affected by armed conflicts. The lack of standardized medical guidance ex...
BACKGROUND: The Pediatric Assessment Triangle (PAT) is a rapid visual assessment framework designed to support early identification of critically ill ...
OBJECTIVE: Rapid advancements in artificial intelligence (AI) technologies offer new opportunities in medical education. The aim of this study is to c...
BACKGROUND: Overdose rates in the U.S. rose dramatically during the COVID-19 pandemic. Well-documented racial and sociodemographic inequities in the i...
Most existing intracranial hematoma segmentation models target acute hemorrhages and may not generalize to the heterogeneous morphology of chronic sub...
The Urgent and Emergency Care system generates a wealth of clinical information, but our ability to harness this for public health planning and to add...
High-entropy materials (HEMs) represent a paradigm shift in developing efficient components of proton exchange membrane fuel cells/water electrolysis ...
BACKGROUND: Self-harm, defined as non-fatal self-inflicted harm regardless of suicidal intent, is a critical global health issue influenced by the int...
OBJECTIVES: Early sepsis and stroke recognition by emergency medical services (EMS) improves triage, treatment, and patient outcomes. Machine learning...
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease is highly prevalent in adults with type 2 diabetes, and advanced fibrosis is its ...
In this cross-sectional observational study, we developed a computer vision-based machine-learning model to estimate headache pain intensity from faci...
Accurate and rapid delineation of diffuse gliomas is essential in emergency neuro-oncology, yet MRI is often unavailable. We present a deep-learning s...
BACKGROUND: AI-based burn depth assessment is rapidly emerging, yet evidence for diagnostic accuracy, generalizability, and deployment readiness remai...
Dyspnea is the subjective sensation of breathing discomfort. This symptom is highly prevalent in patients with chronic and critical illness, and its p...
Imaging after ischemic and hemorrhagic stroke may allow measurement of key phenotypes of injury and recovery for which targeted therapies are still la...