Emergency Medicine

Evaluating GPT-4 Performance on Plastic Surgery Oral Examination Vignettes: A Quantitative and Qualitative Analysis.

BACKGROUND: The use of artificial intelligence (AI) in medicine is rapidly evolving. However, its role in plastic and reconstructive surgery remains underexplored. Plastic surgery requires nuanced, dynamic decision making, and individualized care, making AI integration challenging. This study evaluates GPT-4's ability to respond to American Board of Plastic Surgery (ABPS) Oral Board-style clinical...

Prediction of the relationship between childhood trauma and psychological disturbances among younger adults using machine learning algorithms.

Prediction of Adverse Events in Single Ventricle Physiology Infants Using Artificial Intelligence Tools.

BACKGROUND: Adverse events (AEs) within the cardiac ICU (CICU) are associated with high mortality and comorbidity. Patients with single ventricle (SV)...

Early individualized risk prediction using clinical data for children during the febrile phase of dengue in outpatient settings in Vietnam and Thailand.

Dengue severity prediction models are usually developed using hospitalized patient data, but triage and hospital admission are mainly evaluated in out...

Perceptions and knowledge of machine learning for paediatric related decision support in emergency care - A UK and Ireland network survey study of clinician leaders.

This study explores clinician leaders understanding and perception at site level towards machine learning (ML) decision support tools for paediatric r...

Improving drug identification in overdose death surveillance by using clinical natural language processing models.

The rising rate of drug-related deaths in the United States, largely driven by fentanyl, requires timely and accurate surveillance. However, critical ...

An integrated study combining network toxicology machine learning and molecular simulation reveals the molecular mechanisms of permanent hair dyes in breast cancer.

Permanent hair dyes have been linked to an increased risk of breast cancer (BC), though the underlying mechanisms remain unclear. To address this know...

Current perspectives in cardiogenic shock.

Cardiogenic shock (CS) remains a leading cause of death in intensive cardiac care. Outcomes are limited by delayed recognition of hypoperfusion, heter...

Commercial AI platforms improve reproducibility but fail to meet clinical precision thresholds for intracranial aneurysm measurement: implications for serial surveillance.

BACKGROUND: Intracranial aneurysm affect 3-7% of the global population, with rupture causing > 80% of non-traumatic subarachnoid hemorrhage and approx...

Artificial intelligence in emergency surgery: a scoping review within the artificial intelligence in emergency and trauma surgery (ARIES) project.

AIM: To map and critically appraise the current literature on Artificial Intelligence (AI) applications in emergency general surgery, with a focus on ...

Artificial intelligence for fracture detection on computed tomography: a comprehensive systematic review and meta-analysis of diagnostic test accuracy in non-commercial and commercial solutions.

Rising patient volumes, the increasing use of computed tomography (CT) imaging in emergency departments and the resulting prolonged waiting times high...

Identifying genome-by-childhood trauma interactions for depression using a forest-based approach in the UK Biobank and Adolescent Brain Cognitive Development Study.

Depression is shaped by both genetic and environmental factors, but genome-wide interaction studies (GWIS) often lack power to detect complex gene-env...

Predicting recurrent cardiac arrest within one year after surviving In-Hospital Cardiac Arrest using a machine learning model.

BACKGROUND: Patients discharged alive after in-hospital cardiac arrest (IHCA) have an increased mortality up to a year after hospital discharge. Impro...

Artificial intelligence in virtual fracture clinics: a systematic review of imaging and clinical-text tools.

BACKGROUND: Virtual fracture clinics (VFCs) are a well-established component of acute orthopedic management pathways. Artificial intelligence (AI) hea...

Development and validation of interpretable machine learning models for predicting long-term functional outcomes in elderly patients with aneurysmal subarachnoid hemorrhage.

BACKGROUND: Accurate prognostication in elderly patients with aneurysmal subarachnoid hemorrhage (aSAH) remains challenging due to high morbidity and ...

Remote sensing analysis of forest fire impacts on ecosystem productivity, greenhouse gas emissions, and fire risk in Pakistan.

This study investigates the spatial variability of forest fire intensity, burn indices, ecosystem productivity, and Greenhouse Gas (GHG) emissions in ...

A large language model for complex cardiology care.

The scarcity of subspecialist medical expertise poses a considerable challenge for healthcare delivery. This issue is particularly acute in cardiology...

Comparative analysis of key phagocytic genes in humans and mice using machine learning integrated with single-cell RNA sequencing.

Microglial phagocytosis is essential for neurological recovery after intracerebral hemorrhage (ICH). Using single-cell RNA sequencing, we compared mic...

Automated diagnostic of cervical spondylosis on multimodal medical images with a multi-task deep learning model.

Cervical spondylosis is one of the most common degenerative diseases, seriously affecting life quality. Unlike diseases with explicit lesions like can...

MARVpred: machine learning prediction of inhibitors targeting Marburg virus Gene 4 Small ORF protein.

The Marburg virus (MARV), responsible for severe hemorrhagic fevers with mortality rates as high as 90%, remains a significant public health threat. T...