Emergency Medicine

Can AI improve triage quality? A preliminary assessment of ChatGPT performance in evaluating triage decisions.

BACKGROUND: Ensuring accuracy and consistency in emergency department (ED) triage is vital to patient safety. Despite the presence of standardized protocols, variability in triage decisions remains a challenge. This study explores the potential of ChatGPT, a large language model (LLM), as a retrospective evaluator to assess the appropriateness of nurse-assigned triage levels according to the Tusca...

A Comprehensive Study to Compare Different Compound Representations for Predicting Carcinogenicity In Vivo.

Carcinogenicity evaluation is a critical component of chemical risk assessment, yet traditional in vivo testing remains time consuming, costly, and ethically challenging. Computational approaches based on machine learning offer promising alternatives, but the relative contributions of different molecular representation strategies for predicting in vivo carcinogenicity remain insufficiently explore...

MMP9 mediates 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-induced osteoarthritis cartilage damage via regulating the p38 MAPK pathway: A mechanistic study based on network toxicology and multi-omics.

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), a highly toxic persistent organic pollutant (POP), is associated with musculoskeletal disorders, yet its m...

G-PARC: Graph-Physics Aware Recurrent Convolutional neural networks for spatiotemporal dynamics on unstructured meshes.

Physics-aware recurrent convolutional networks (PARC) have demonstrated strong performance in predicting nonlinear spatiotemporal dynamics by embeddin...

ML-based electromyography signal analysis for assessing rehabilitation exercise execution quality.

Following lower limb trauma, performing orthopaedic rehabilitation exercises is a crucial factor in successful recovery. However, many patients find i...

An EEG-based hybrid machine learning approach for CT scan triage in mild traumatic brain injury.

Mild traumatic brain injury (mTBI) frequently prompts computed tomography (CT) imaging in emergency departments, despite a high proportion of negative...

A review of bloodstream infections-pathogens, pathogenesis, diagnostic strategies, treatment methods-challenges and future aspects.

PURPOSE: Bloodstream infections (BSIs) remain a major cause of morbidity and mortality worldwide and continue to represent a substantial challenge to ...

Machine learning for risk stratification of hypertensive disorders of pregnancy: Enhancing clinical efficiency in low-resource antenatal care in Tanzania.

Maternal mortality in Tanzania remains a public health crisis, with Hypertensive Disorders of Pregnancy (HDP) causing 34% of direct obstetric deaths. ...

Unraveling the adverse outcome pathways of metabolic dysfunction-associated steatotic liver disease triggered by environmental mixtures via biological knowledge-driven machine learning.

Environmental pollutant mixtures are potential risk factors for metabolic dysfunction-associated steatotic liver disease (MASLD), yet their joint effe...

Optimising pain identification in resource-limited emergency departments using transfer learning and fine-tuned language models.

OBJECTIVE: To optimise the identification of patients presenting with pain in emergency department (ED) settings with limited resources using multiple...

TRUST: an large language model-based dialogue system for trauma understanding and structured assessments.

OBJECTIVES: While large language models (LLMs) have been widely used to assist clinicians and support patients, no existing work has explored dialogue...

Prioritising parameters to minimise blood transfusions for postpartum haemorrhage: A machine learning study using national population data.

BACKGROUND: Postpartum haemorrhage (PPH) accounts for approximately 27% of maternal deaths worldwide and often requires blood transfusion, along with ...

Development and Validation of Machine Learning Models to Optimize Imaging and Referrals for Dizziness in the Emergency Department.

BACKGROUND: Dizziness and vertigo are common emergency department (ED) presentations, but only 2%-5% receive a serious diagnosis, such as stroke or tr...

Improving mortality prediction in critically ill cancer patients with a multidimensional machine learning model.

BACKGROUND: Prognostic assessment in critically ill cancer patients is challenging due to the suboptimal performance of traditional severity scores. W...

TCEP Induces Liver Injury Through Suppression of the PI3K/AKT Axis: Integrated Evidence from Epidemiology, Network Toxicology, and In Vivo Validation.

Tris(2-chloroethyl) phosphate (TCEP) is a widely used chlorinated organophosphate flame retardant, but its role in liver injury remains unclear. In th...

AI in clinical diagnostics in dermatology: applications, validation, and real-world use cases.

Artificial intelligence (AI) has moved from proof-of-concept studies in dermatology to selective, real-world clinical use, particularly in image-based...

Artificial Intelligence in skin disease therapeutics: from drug discovery to personalized treatment pathways.

Artificial intelligence (AI) comprises computational methods capable of tasks associated with human cognition, and includes specialized subfields such...

Artificial Intelligence in Disaster Triage: Enhancing Emergency Nursing Practice Through Decision Support.

Effective triage during mass casualty incidents is critical, requiring emergency nurses to make rapid decisions in high-stress, resource-limited envir...

Comparing the Efficacy Between ChatGPT 5, Grok 3, and Claude 4.5 Sonnet in Analyzing Orthopedic Trauma-Related Imaging.

OBJECTIVES: To evaluate and compare the ability of three popular open-source artificial intelligence platforms to diagnose common trauma-related fract...

Building centaur responders: is emergency management ready for artificial intelligence?

This article examines the preparedness of emergency management (EM) for addressing questions pertaining to artificial intelligence (AI), encompassing ...