AIMC Topic: Emergency Medicine

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A Review of Natural Language Processing in Medical Education.

The western journal of emergency medicine
Natural language processing (NLP) aims to program machines to interpret human language as humans do. It could quantify aspects of medical education that were previously amenable only to qualitative methods. The application of NLP to medical education...

Deep neural network improves fracture detection by clinicians.

Proceedings of the National Academy of Sciences of the United States of America
Suspected fractures are among the most common reasons for patients to visit emergency departments (EDs), and X-ray imaging is the primary diagnostic tool used by clinicians to assess patients for fractures. Missing a fracture in a radiograph often ha...

Validation of deep-learning-based triage and acuity score using a large national dataset.

PloS one
AIM: Triage is important in identifying high-risk patients amongst many less urgent patients as emergency department (ED) overcrowding has become a national crisis recently. This study aims to validate that a Deep-learning-based Triage and Acuity Sco...

Artificial intelligence and machine learning in emergency medicine.

Emergency medicine Australasia : EMA
Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in ...

Identification of Long Bone Fractures in Radiology Reports Using Natural Language Processing to support Healthcare Quality Improvement.

Applied clinical informatics
BACKGROUND: Important information to support healthcare quality improvement is often recorded in free text documents such as radiology reports. Natural language processing (NLP) methods may help extract this information, but these methods have rarely...

A Novel Artificial Intelligence System for Endotracheal Intubation.

Prehospital emergency care
OBJECTIVE: Adequate visualization of the glottic opening is a key factor to successful endotracheal intubation (ETI); however, few objective tools exist to help guide providers' ETI attempts toward the glottic opening in real-time. Machine learning/a...

Reducing the incidence of oxyhaemoglobin desaturation during rapid sequence intubation in a paediatric emergency department.

BMJ quality & safety
OBJECTIVES: Rapid sequence intubation (RSI) is the standard for definitive airway management in emergency medicine. In a video-based study of RSI in a paediatric emergency department (ED), we reported a high degree of process variation and frequent a...

The Quebec rural emergency department project: a cross-sectional study of a potential two-pronged strategy in the knowledge transfer process.

PloS one
INTRODUCTION: Health services research generates useful knowledge. Promotion of implementation of this knowledge in medical practice is essential. Prior to initiation of a major study on rural emergency departments (EDs), we deployed two knowledge tr...

Evaluating large language and large reasoning models as decision support tools in emergency internal medicine.

Computers in biology and medicine
BACKGROUND: Large Language Models (LLMs) hold promise for clinical decision support, but their real-world performance varies. We compared three leading models (OpenAI's "o1" Large Reasoning Model (LRM), Anthropic's Claude-3.5-Sonnet, and Meta's Llama...