Emergency Medicine

Clin-STAR Corner: Practice Changing Advances at the Interface of Artificial Intelligence/Machine Learning and Geriatrics.

Artificial intelligence (AI) methods, including machine learning (ML), are transforming healthcare by enabling personalized interventions that integrate multimodal data to support rehabilitation, preventive care, and remote monitoring. Despite their broad potential, older adults remain underrepresented in model development, raising concerns about bias and limited generalizability. As AI/ML adoptio...

AI-based automated bleeding monitoring in conventional and robot-assisted laparoscopic surgery: a systematic review.

Artificial intelligence has emerged as a promising approach for improving the detection and management of intraoperative bleeding during conventional and robotic-assisted laparoscopic surgery, where delayed recognition of hemorrhage can lead to increased morbidity and procedural complexity. This review synthesizes current evidence on the use of artificial intelligence for intraoperative bleeding m...

Developing and validating an artificial intelligence-based electronic triage model for predicting clinical outcomes among cardiac-suspected patients in the emergency department.

AIMS: Emergency department overcrowding, especially in cardiac units, delays care and raises mortality. Conventional triage is error-prone. We develop...

Development of an interpretable machine learning model for predicting stress urinary incontinence following the delivery of a first child.

INTRODUCTION: The goal of this research is to use machine learning (ML) techniques to create a risk prediction model for postpartum stress urinary inc...

Predicting poor wrist function recovery after internal fixation of distal radius fractures: A retrospective study based on multivariate analysis and machine learning models.

This study aimed to identify independent risk factors associated with poor wrist function recovery 6 months after internal fixation of distal radius f...

Zebrafish Behavioral Phenotyping: Current Assays, Automated Platforms, and the Emerging Need for AI in Neuroscience, Drug Discovery and Toxicology.

Over the past two decades, zebrafish have become an increasingly prominent model organism for basic research, drug discovery, and toxicology. Its adva...

A rapid total-body PET imaging approach for pediatric patients using non-attenuation-corrected PET scans.

BACKGROUND: Pediatric lymphoma patients undergo multiple 18F-FDG PET/CT examinations for staging and response assessment, raising concerns about cumul...

Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.

BACKGROUND: Infections are a leading cause of hospitalizations and emergency department (ED) visits in home care. Existing prediction tools often unde...

Rising prevalence of drone attacks in warfare and terrorism and the implications for medical management of major incidents.

As drone technology advances, the likelihood of their use in terrorism increases. The emergence of artificial intelligence-controlled drone swarms and...

AI assisted detection of large vessel occlusion on non-contrast CT: multinational validation and reader study.

OBJECTIVE: Early detection of large vessel occlusion (LVO) on non-contrast CT (NCCT) could accelerate stroke triage, but NCCT based artificial intelli...

Automated orbital wall fracture detection and severity classification of ocular injuries using deep learning.

Orbital fractures are frequently encountered in maxillofacial trauma and can be associated with severe globe injuries (SGI). Accurate and timely diagn...

Random Forest Model for the Prediction of Herbal-Induced Liver Injury: Application to Molecules from the West African Pharmacopoeia.

Herbal medicines play a crucial role in primary healthcare across West Africa, yet their potential for liver toxicity remains poorly documented. Predi...

Exploring a Large Language Model-Based Chatbot Use in Data Analysis: A Case Study of the Problems Related to the Do Not Attempt Resuscitation Order.

The interpretative framework was used to explore how healthcare professionals (HCPs), artificial intelligence (AI), and researchers construct and nego...

Uncovering Topics in Dutch Patient Messages in Inflammatory Bowel Disease: A Comparative Study of Embedding Models for Neural Topic Modeling.

This study applied natural language processing to identify common topics in 12,054 Dutch patient-provider messages in inflammatory bowel disease. Usin...

A Tool for Virtual Nursing Utilization in Emergency Reception: An Exploratory Machine Learning Approach.

As virtual nursing (VN) gains traction as a scalable solution to support hospital workflows, identifying patients best suited for VN admission assessm...

Proximal Junctional Kyphosis Prevention in Adult Spinal Deformity Surgery: A Technical Review of Tethering and Adjunctive Strategies.

BACKGROUND AND OBJECTIVES: Proximal junctional kyphosis (PJK) and proximal junctional failure (PJF) remain significant complications after long-segmen...

Automated abdominal aortic calcification, muscle health and incident falls: the UK Biobank Imaging Study.

Emerging evidence suggests that vascular disease is linked with poorer muscle strength and higher falls risk. We evaluated the association between abd...

AI-Enhanced Nanophotonic Heterochain Sensor Enables Multiplexed Biomarker Detection across Serum, Urine, and Saliva for Stroke Differentiation.

Background: Rapid and accurate identification of stroke subtype is critical for timely intervention, yet current diagnostic assays are limited by long...

Evaluating Postpartum Hemorrhage Transfusion Risk with a Machine Learning Model for Informed Consent: Retrospective Cohort Study.

BACKGROUND: Postpartum hemorrhage requiring a blood transfusion is a concern for patients and clinicians. Postpartum hemorrhage risk and mode of deliv...

Impact of COVID-19-related data drift on machine-learning prognostic models predicting 30-day opioid-related emergency department visits, hospitalisation or mortality: a population-level administrative data study in Alberta, Canada.

OBJECTIVE: To develop machine-learning (ML) models during the COVID-19 pandemic and adjacent time periods to evaluate the impact of data drift on mode...