Emergency Medicine

Development of machine learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma.

The aim of this study was to develop a machine-learning prediction model for AKI after craniotomy an...

Deep Learning Reconstruction in Abdominopelvic Contrast-Enhanced CT for The Evaluation of Hemorrhages.

PURPOSE: To investigate the effects of deep learning reconstruction on depicting arteries and provid...

Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Study-level Labels.

Purpose To develop a highly generalizable weakly supervised model to automatically detect and locali...

Comparing answers of artificial intelligence systems and clinical toxicologists to questions about poisoning: Can their answers be distinguished?

OBJECTIVE: To present questions about poisoning to 4 artificial intelligence (AI) systems and 4 clin...

Accelerated Chest Pain Treatment With Artificial Intelligence-Informed, Risk-Driven Triage.

Deep Learning to Detect Intracranial Hemorrhage in a National Teleradiology Program and the Impact on Interpretation Time.

The diagnostic performance of an artificial intelligence (AI) clinical decision support solution for...

Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence.

OBJECTIVES: This study evaluated the performance of the YOLOv5 deep learning model in detecting diff...

Artificial Intelligence in Military Medicine.

Artificial intelligence (AI) has garnered significant attention for its pivotal role in the national...

[Study on automatic and rapid diagnosis of distal radius fracture by X-ray].

This article aims to combine deep learning with image analysis technology and propose an effective c...

Emergency Department Length of Stay Classification Based on Ensemble Methods and Rule Extraction.

This study employs machine learning techniques to identify factors that influence extended Emergency...

Remote Monitoring, AI, Machine Learning and Mobile Ultrasound Integration upon 5G Internet in the Prehospital Care to Support the Golden Hour Principle and Optimize Outcomes in Severe Trauma and Emergency Surgery.

AIM: Feasibility and reliability evaluation of 5G internet networks (5G IN) upon Artificial Intellig...

Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach.

Emergency department (ED) overcrowding is a complex problem that is intricately linked with the oper...

Harnessing the Power of Machine Learning and Electronic Health Records to Support Child Abuse and Neglect Identification in Emergency Department Settings.

Emergency departments (EDs) are pivotal in detecting child abuse and neglect, but this task is often...

[Artificial intelligence in emergency radiology: fiction or reality?].

Artificial intelligence (AI) is a rapidly advancing technology in our society. The emergency radiolo...

Fracture risk prediction in postmenopausal women with traditional and machine learning models in a nationwide, prospective cohort study in Switzerland with validation in the UK Biobank.

Fracture prediction is essential in managing patients with osteoporosis and is an integral component...

[Research progress on the application of artificial intelligence in the early diagnosis and treatment of burn diseases].

Artificial intelligence (AI) technology is advancing rapidly, constantly presenting its application ...

The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study.

BACKGROUND: Artificial intelligence (AI) applications in health care have been effective in many are...

The potential impact of artificial intelligence on emergency department overcrowding and access block.

Trainee Focus debate: Artificial intelligence will have a negative impact on emergency medicine.

Trainee Focus debate: Artificial intelligence will have a positive impact on emergency medicine.

Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models.

OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for effi...