Emergency Medicine

Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data.

INTRODUCTION: Intracerebral hemorrhage represents 15 % of all strokes and it is associated with a hi...

Unlocking the adaptive advantage: correlation and machine learning classification to identify optimal online adaptive stereotactic partial breast candidates.

Online adaptive radiotherapy (OART) is a promising technique for delivering stereotactic accelerated...

Identification of biological indicators for human exposure toxicology in smart cities based on public health data and deep learning.

With the acceleration of urbanization, the risk of urban population exposure to environmental pollut...

Assessing the utility of artificial intelligence throughout the triage outpatients: a prospective randomized controlled clinical study.

Currently, there are still many patients who require outpatient triage assistance. ChatGPT, a natura...

Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs.

Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in s...

Fillable Magnetic Microrobots for Drug Delivery to Cardiac Tissues In Vitro.

Many cardiac diseases, such as arrhythmia or cardiogenic shock, cause irregular beating patterns tha...

Artificial Intelligence: Can It Save Lives, Hospitals, and Lung Screening?

BACKGROUND: Early detection is essential in lung cancer survival. Lung screening or incidental detec...

Knowledge, attitudes and practices of using Indocyanine Green (ICG) fluorescence in emergency surgery: an international web-based survey in the ARtificial Intelligence in Emergency and trauma Surgery (ARIES)-WSES project.

Fluorescence imaging is a real-time intraoperative navigation modality to enhance surgical vision an...

Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study.

BACKGROUND: Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognos...

Proximal femur fracture detection on plain radiography via feature pyramid networks.

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence proje...

Implementation considerations for the adoption of artificial intelligence in the emergency department.

OBJECTIVE: Artificial intelligence (AI) has emerged as a potentially transformative force, particula...

Artificial Intelligence in Emergency Medicine: A Case for More.

Improved differentiation of cavernous malformation and acute intraparenchymal hemorrhage on CT using an AI algorithm.

This study aimed to evaluate the utility of an artificial intelligence (AI) algorithm in differentia...

The AI Future of Emergency Medicine.

In the coming years, artificial intelligence (AI) and machine learning will likely give rise to prof...

Machine learning models for predicting early hemorrhage progression in traumatic brain injury.

This study explores the progression of intracerebral hemorrhage (ICH) in patients with mild to moder...

Hepatic toxicity prediction of bisphenol analogs by machine learning strategy.

Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a p...

Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning.

Evaluation of skin recovery is an important step in the treatment of burns. However, conventional me...

Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar.

The large number and scale of natural and man-made disasters have led to an urgent demand for techno...

Using a clinical narrative-aware pre-trained language model for predicting emergency department patient disposition and unscheduled return visits.

The increasing prevalence of overcrowding in Emergency Departments (EDs) threatens the effective del...

Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review.

OBJECTIVE: The aim of this study was to summarize the literature on the applications of machine lear...

Can artificial intelligence help ED nurses more accurately triage patients?

The Emergency Severity Index (ESI) is the most popular tool used to triage patients in the US and ab...