Latest AI and machine learning research in emergency medicine for healthcare professionals.
Our purpose was to evaluate the approach of two different chatbots (ChatGPT and Gemini) to a list of questions about emergency department as a border area between hospital and territory. This study was performed in a single day, on 3 March 2024. Two different questions were sequentially type in Italian language, the same for each chatbot: definition of the emergency department as a border zone in ...
Self-supervised foundation models have recently been successfully extended to encode three-dimensional (3D) computed tomography (CT) images, with excellent performance across several downstream tasks, such as intracranial hemorrhage detection and lung cancer risk forecasting. However, as self-supervised models learn from complex data distributions, questions arise concerning whether these embedd...
Proximal humeral fractures are among the most common fractures seen in emergency departments. Accurately diagnosing and selecting the most appropriate...
Natural Language Processing (NLP) has shown promise in fields like radiology for converting unstructured into structured data, but acquiring suitable ...
Burn injuries present a significant global health challenge. Among the most severe long-term consequences are contractures, which can lead to functi...
Stress distributions and the corresponding fracture patterns and evolutions in the microstructures strongly influence the load-carrying capabilities...
Identifying predictive features from high-dimensional datasets is a major task in biomedical research. However, it is difficult to determine the rob...
Differential diagnosis is crucial for medicine as it helps healthcare providers systematically distinguish between conditions that share similar sym...
Intracranial Hemorrhage is a potentially lethal condition whose manifestation is vastly diverse and shifts across clinical centers worldwide. Deep-l...
Purpose To develop a highly generalizable weakly supervised model to automatically detect and localize image-level intracranial hemorrhage (ICH) by us...
PURPOSE: To investigate the effects of deep learning reconstruction on depicting arteries and providing suitable images for the evaluation of hemorrha...
The aim of this study was to develop a machine-learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma....
OBJECTIVES: We assessed whether proactive care management for artificial intelligence (AI)-identified at-risk patients reduced preventable emergency d...
Background It is unclear whether artificial intelligence (AI) explanations help or hurt radiologists and other physicians in AI-assisted radiologic di...
Artificial intelligence (AI) is increasingly significant in neurosurgery, enhancing differential diagnosis, preoperative evaluation, and surgical prec...
We propose a workflow based on physics-informed neural networks (PINNs) to model multiphase fluid flow in fractured porous media. After validating t...
In this paper, we address the vision-based autonomous landing problem in complex urban environments using deep neural networks for semantic segmenta...
We present the TRIAGE Benchmark, a novel machine ethics (ME) benchmark that tests LLMs' ability to make ethical decisions during mass casualty incid...
3D Gaussian splatting (3DGS), known for its groundbreaking performance and efficiency, has become a dominant 3D representation and brought progress ...
OBJECTIVE: To present questions about poisoning to 4 artificial intelligence (AI) systems and 4 clinical toxicologists and determine whether readers c...