Emergency Medicine

Machine learning-based model for triage-stage prediction of emergency department disposition.

Cross-Platform Proteomics and Machine Learning Algorithms Nominate Plasma Biomarkers of Stroke Diagnosis.

BACKGROUND: Blood-based biomarkers for stroke subtyping could improve triage in emergency settings. We used cross-platform proteomics to identify plasma biomarkers differentiating major stroke diagnostic groups. METHODS: We conducted a case-control study using 2 biorepositories. Plasma was collected in the emergency department from adults with suspected stroke before therapeutic intervention. Diff...

AI-Aided Triage for GSWH: Validating an Interpretable HCT-Based Mortality Model.

Civilian gunshot wounds to the head (GSWH) carry high mortality yet lack standardized, imaging-based triage tools. Because initial noncontrast head co...

Artificial Intelligence Models for Predicting Triage in Emergency Departments: Seven-Month Retrospective Comparative Study of Natural Language Processing, Large Language Model, and Joint Embedding Predictive Architectures.

BACKGROUND: Triage errors in emergency departments (EDs), including undertriage and overtriage, pose significant risks to patient safety and resource ...

Clinical implementation of AI for vertebral fracture detection in CT aligned with fracture liaison services: high prevalence of undiagnosed vertebral fractures.

UNLABELLED: We assessed feasibility and effectiveness of AI-based VF screening in CT, integrated with a local FLS. The system identified VFs in 14% of...

Non-contact monitoring in neonatology.

To present and critically evaluate current contactless monitoring modalities - infrared thermography (IRT), photoplethysmography imaging (PPGI), balli...

Risk prediction modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: leveraging machine learning.

BACKGROUND: Preprocedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-makin...

Clinical Impact of Postrecanalization Hemorrhagic Transformation and Its Prediction Using Baseline Noncontrast CT.

BACKGROUND: Hemorrhagic transformation (HT) after recanalization therapy remains a critical concern in acute ischemic stroke management. While severe ...

Predictive modeling of composite fracture toughness using machine learning.

OBJECTIVES: This study applied machine learning (ML) models to predict fracture toughness (K1c) of experimental ion-releasing resin-based composites a...

The Role of Artificial Intelligence for Intimate Partner Violence Prevention: A Systematic Review.

INTRODUCTION: Intimate partner violence (IPV), encompassing physical, sexual, emotional and economic abuse, remains a pervasive global health concern....

Benchmarking a deep learning model against healthcare practitioners for hip fracture detection in the emergency department.

INTRODUCTION: This study aimed to validate a deep learning (DL) model for automated hip fracture detection on pelvic X-rays in emergency departments (...

Artificial intelligence and robotics in global neurosurgery: A scoping review.

OBJECTIVE: Artificial intelligence (AI) and robotics are transforming neurosurgical care; however, the application of these technologies in low- and m...

External validation of an AI-Based fracture detection tool for hip and pelvic radiographs in a multicenter retrospective cohort.

OBJECTIVE: Artificial intelligence tools show promise in fracture detection but may be impaired by hidden stratification. We aim to evaluate the diagn...

Deviation From AI-Predicted Implant Sizes in Total Hip Arthroplasty Is Associated With Increased Complication Risk.

BACKGROUND: Patient characteristics may predict implant sizes in total hip arthroplasty (THA), but the clinical value and generalizability of such mod...

Network toxicology integrated with machine learning and SHAP analysis identifies overlapping immune signatures between Di(2-ethylhexyl) phthalate (DEHP) and Sjögren's syndrome.

Accuracy of GPT-5 and GPT-4o in diagnosing STEMI from 12-Lead ECGs: A comparative study with cardiologists and emergency physicians.

BACKGROUND: ST-segment elevation myocardial infarction (STEMI) requires rapid, accurate electrocardiogram (ECG) interpretation. The diagnostic effecti...

6PPD-quinone promotes ovarian cancer progression: Insights from network toxicology, machine learning, and in vitro validation.

6PPD-quinone (6PPD-Q), a toxic tire-derived antioxidant transformation product, is a pervasive environmental contaminant linked to reproductive toxici...

Evaluating Generative Artificial Intelligence Models' Responses to Questions About Scaphoid Fracture and Scaphoid Nonunion.

PURPOSE: To evaluate and compare the responses of ChatGPT and Google Gemini to common patient questions about scaphoid fracture and scaphoid nonunion,...

Triage Nurses' Perceptions of Artificial Intelligence: A Cross-Sectional Study.

INTRODUCTION: Emergency department crowding and increasing patient complexity challenge traditional triage models. Artificial intelligence may support...

Rejoining fragmented ancient bamboo slips with physics-driven deep learning.

Bamboo slips are a crucial medium for recording ancient civilizations in East Asia. However, many excavated bamboo slips have been fragmented into tho...