Emergency Medicine

Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.

Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fatalities were collected from forensic and hospital records. K-means clustering grouped death locations and identified cluster centres. The Ball Tree al...

Bridging Data Gaps in Emergency Care: The NIGHTINGALE Project and the Future of AI in Mass Casualty Management.

In the context of mass casualty incident (MCI) management, artificial intelligence (AI) represents a promising future, offering potential improvements in processes such as triage, decision support, and resource optimization. However, the effectiveness of AI is heavily reliant on the availability of quality data. Currently, MCI data are scarce and difficult to obtain, as critical information regard...

Domestic violence and childhood trauma among married women using machine learning approach: a cross-sectional study.

BACKGROUND: Globally, 27% of ever-partnered women aged 15-49 have experienced physical, sexual, or intimate partner violence at least once in their li...

The role of artificial intelligence in predicting injured structures based on clinical images of lacerations in the volar aspect of the hand and forearm.

INTRODUCTION: Recently introduced image processing capabilities of AI models, which are accessible to a broad audience, may contribute to progress in ...

Semantic-consistent diffusion model for unsupervised traumatic brain injury detection and segmentation from computed tomography images.

BACKGROUND: Unsupervised traumatic brain injury (TBI) lesion detection aims to identify and segment abnormal regions, such as cerebral edema and hemor...

AI-enhanced resuscitation image production training: advancing diversity and inclusion with GPT-4o.

Improving the usability of large emergency 911 data reporting systems: A machine learning case study using emergency incident descriptions.

INTRODUCTION: Emergency 9-1-1 incident data are recorded voluntarily within fire-department-specific computer-aided dispatch systems. The National Fir...

Parametric-MAA: fast, object-centric avoidance of metal artifacts for intraoperative CBCT.

PURPOSE: Metal artifacts remain a persistent issue in intraoperative CBCT imaging. Particularly in orthopedic and trauma applications, these artifacts...

Prioritization strategies for non-target screening in environmental samples by chromatography - High-resolution mass spectrometry: A tutorial.

Non-target screening (NTS) using chromatography coupled to high-resolution mass spectrometry (HRMS), has become fundamental for detecting and prioriti...

MetaGP: A generative foundation model integrating electronic health records and multimodal imaging for addressing unmet clinical needs.

Artificial intelligence makes strides in specialized diagnostics but faces challenges in complex clinical scenarios, such as rare disease diagnosis an...

A Neural Network Model for Intelligent Classification of Distal Radius Fractures Using Statistical Shape Model Extraction Features.

OBJECTIVE: Distal radius fractures account for 12%-17% of all fractures, with accurate classification being crucial for proper treatment planning. Stu...

Communication challenges and experiences between parents and providers in South Korean paediatric emergency departments: a qualitative study to define AI-assisted communication agents.

OBJECTIVES: This study aimed to explore communication challenges between parents and healthcare providers in paediatric emergency departments (EDs) an...

Evaluation of correctness and reliability of GPT, Bard, and Bing chatbots' responses in basic life support scenarios.

Timely recognition and initiation of basic life support (BLS) before emergency medical services arrive significantly improve survival rates and neurol...

Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children.

Disseminated intravascular coagulation (DIC) is a thrombo-hemorrhagic disorder that can be life-threatening in critically ill children, and the quest ...

Updates in Neonatal Seizures.

Neonatal seizures are a common medical emergency, necessitating prompt treatment. The most common etiologies include hypoxic-ischemic encephalopathy, ...

Machine Learning-Guided Fluid Resuscitation for Acute Pancreatitis Improves Outcomes.

INTRODUCTION: Ariel Dynamic Acute Pancreatitis Tracker (ADAPT) is an artificial intelligence tool using mathematical algorithms to predict severity an...

Can Artificial Intelligence Language Models Effectively Address Dental Trauma Questions?

BACKGROUND/AIM: Artificial intelligence (AI) chatbots, also known as large language models (LLMs), have become increasingly common educational tools i...

Identification of the potential role of PANoptosis-related genes in burns via bioinformatic analyses and experimental validation.

BACKGROUND: The treatment of burns is highly challenging due to their complex pathophysiological mechanisms. PANoptosis, as an important form of cell ...

AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiogr...

Triage of Patient Messages Sent to the Eye Clinic via the Electronic Medical Record: A Comparative Study on AI and Human Triage Performance.

: Assess the ability of ChatGPT-4 (GPT-4) to effectively triage patient messages sent to the general eye clinic at our institution. : Patient messages...