Emergency Medicine

The Ethics Committee of the Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI) - Artificial intelligence in end-of-life decision-making processes: ethical reflections.

Recent proposals to use artificial intelligence (AI) in end-of-life decision-making for incapacitated patients without advance directives have prompted critical reflection by the Ethics Committee of the Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI). This position paper analyzes both general ethical concerns surrounding AI in clinical practice and specific iss...

Examining the utility of digital phenotyping for the prediction of intrusive experiences.

Background: Intrusive experiences related to witnessing a traumatic event are the core symptom of post-traumatic stress disorder (PTSD), and have been shown to be predicted by peritraumatic emotional arousal. However, research into the role of peritraumatic arousal in the development of intrusions has been limited by a reliance on self-report scales.Objective: This study aimed to examine whether f...

Emerging Applications and Key Considerations on the Use of Artificial Intelligence in Forensic Medicine and Pathology.

Artificial intelligence (AI) has rapidly expanded across medicine, demonstrating value in image analysis, risk prediction, and data interpretation. In...

A Lightweight LSTM-Transformer Fusion Architecture for Real-Time Sepsis Mortality Prediction.

BackgroundAccurate prediction of short-term mortality in sepsis patients is critical for timely clinical decision-making. However, existing deep learn...

Geometric Morphometric-Based Estimation as an Aid for Naso-Orbito-Ethmoidal Trauma Management.

The naso-orbito-ethmoid (NOE) region comprises complex anatomy, and as such, NOE fractures present with a challenge during reconstruction. Restoring t...

Evaluating the Potential Impact of AI on Urinary Tract Infection Diagnosis in the Emergency Department Across Demographic Groups: Retrospective Cohort Study.

BACKGROUND: Urinary tract infection (UTI) is a common emergency department (ED) presentation but can be challenging to diagnose; both overdiagnosis an...

National Institute of Allergy and Infectious Diseases (NIAID) - Bundeswehr Institute of Radiobiology (BIR) Joint Programmatic Workshop on a Global Approach to Medical Countermeasure Development in Radiological Public Health Preparedness.

This invited commentary grew out of a presentation made at the 2025 ConRad Meeting in Munich, Germany, and summarizes talks made by researchers suppor...

Beyond NIHSS and neuroimaging: an interpretable gradient boosting model for predicting in-hospital mortality in ICU patients with acute ischemic stroke.

This study aimed to develop an interpretable machine learning model for predicting in-hospital mortality among acute ischemic stroke (AIS) patients ad...

Artificial Intelligence in the Trauma Bay: A Pilot Comparison With Surgical Trainees.

BackgroundLarge language models (LLMs) have demonstrated strong performance on general medical knowledge assessments; however, their accuracy within h...

Integrating multi-omics and machine learning to decipher the molecular pathways of bisphenol a-associated lactylation-related genes driving bladder cancer.

In this study, we systematically investigated bladder cancer-related gene signatures using a toxicogenomics-informed framework, with particular attent...

Sodium nitrite promotes atherosclerosis via IL-1β: Network toxicology and machine learning insights.

BACKGROUND: Atherosclerosis (AS) is a major global health burden. Sodium nitrite, a common environmental and dietary contaminant, has been implicated ...

An Integrated AIVIVE-PBPK-QIVIVE Framework with HTTK Validation for Probabilistic Risk Assessment of Neodymium Nitrate.

Rare earth elements (REEs) are critical to modern industries but pose growing health risks due to increasing environmental release, and neodymium nitr...

Machine learning predicts postoperative mortality in a Latin American cardiovascular surgery multicenter cohort: Toward precision risk stratification.

OBJECTIVE: A total of 28% of global cardiac surgeries are performed in Latin America; however, surgeons there are faced with many preventable deaths, ...

Machine Learning Prediction and Reducing Overdoses With Electronic Health Record Nudges (mPROVEN) in the Primary Care Setting: Protocol for a Cluster Randomized Controlled Trial.

BACKGROUND: Opioid overdose remains a leading cause of preventable death in the United States. Existing approaches to identify individuals at elevated...

Breaking the Black Box: Interpretable AI Achieves Superior Hemorrhage Detection with the Compensatory Reserve Measurement.

Hemorrhage remains the leading cause of preventable trauma death, with traditional vital signs failing to detect blood loss until 25-30% volume deplet...

AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges.

The translation of big data analytics and artificial intelligence (AI) into clinical decision support systems (CDSSs) has advanced from proof of conce...

High-Resolution 2D versus 3D Lumbar Spine MRI Optimized with a Deep Learning Reconstruction Algorithm and Prototype Conformal Coil.

BACKGROUND AND PURPOSE: Lumbar spine MRI is predominantly performed using 2D FSE sequences. 3D FSE sequences offer potential advantages over 2D, espec...

Understanding how language barriers in the paediatric emergency care setting influences safety of care delivery: a scoping review.

BACKGROUND: Communication in a family's primary language can support safe care. Vital steps within the care delivery process are contingent on success...

Mitigating algorithmic unfairness arising from forgetfulness of medical records in clinical artificial intelligence.

Protecting patients' right to be forgotten (RtbF) in the digital era underscores algorithms to delete health records from deployed artificial intellig...

Deciphering the potential pathogenic mechanisms of 3-BHA in ovarian cancer through integrated bioinformatics and machine learning strategies.

Ovarian cancer (OC) remains a malignancy characterized by obscure risk factors and unfavorable prognosis. While 3-tert-butyl-4-hydroxyanisole (3-BHA) ...