Emergency Medicine

Human vs. artificial intelligence in medical charting: a comparative study in the simulated emergency medicine context.

OBJECTIVES: The increasing administrative burden associated with electronic health records has contributed to reduced efficiency and rising burnout among physicians, particularly in high-volume environments like Emergency Departments (EDs). Ambient artificial intelligence (AI) scribes offer a potential solution by generating clinical documentation in real-time using ambient listening. This study a...

Machine learning to predict hospital admission at triage in paediatric emergency care: A meta-analysis.

UNLABELLED: Machine learning (ML) models have shown promise improving outcome prediction and early risk stratification in paediatric emergency department (ED) triage. This review aims to evaluate the diagnostic performance of ML in predicting hospital admissions from the data collected at triage in paediatric emergency departments (EDs). Searches were conducted in PubMed, Ovid, Scopus, and Web of ...

Machine learning assisted differentiation of low acuity patients at dispatch: The MADLAD randomized controlled trial.

BACKGROUND: Resource Constrained Situations (RCS) at Emergency Medical Dispatch centers where there are more patients requiring an ambulance than ther...

Artificial Intelligence-Enhanced Cardiac Point-of-Care Ultrasound: A Prospective Single-Arm Study.

OBJECTIVE: To evaluate the clinical utility of combining artificial intelligence (AI) with handheld focused cardiac ultrasound (FoCUS) performed by no...

Technical challenges and quality considerations for dissolving microneedles.

Dissolving microneedles (DMNs) deliver therapeutic molecules by bypassing the stratum corneum upon skin insertion. While DMNs show potential for trans...

Automated abdominal aortic calcification and trabecular bone score independently predict incident fracture during routine osteoporosis screening.

Abdominal aortic calcification (AAC), a marker of subclinical cardiovascular disease, has previously shown to be associated with low BMD and fracture....

Deep learning-assisted otoscopic screening for paediatric otitis media: feasibility of edge deployment.

BACKGROUND: Otitis media is common in children. Otoscopic differentiation of acute otitis media (AOM), otitis media with effusion (OME) and normal tym...

Vocal Biomarkers of Childhood Trauma: A Machine-Learning Approach to Speech Analysis.

PURPOSE: Childhood trauma can disrupt communication, yet early signs often go unrecognized in regions affected by ongoing war, where immediate physica...

Automated hemodynamic management system for the prevention of hypotension and cardiac output reduction during general anesthesia: preclinical experimental validation.

PURPOSE: Intraoperative hypotension and cardiac output (CO) reduction adversely affect outcomes and require cause-specific treatment against vasodilat...

A two-stage deep learning system for cervical spine fracture diagnosis: integrating 3D segmentation and 2.5D classification on CT images.

Machine learning prediction of common bile duct stones using synthetic data to guide emergency ERCP decisions.

Endoscopic retrograde cholangiopancreatography (ERCP) remains the standard for common bile duct (CBD) stone removal. However, current guidelines often...

Artificial intelligence for battlefield triage in large-scale combat operations: Opportunities, limits, and ethical considerations.

Large-scale combat operations (LSCOs) impose major constraints on battlefield medical systems, combining sustained casualty inflow, degraded communica...

Unraveling the toxicological impact of Bisphenol A exposure on dermatomyositis: An integration of network toxicology and machine learning approaches.

BACKGROUND: Dermatomyositis is a common immune-mediated skin disorder whose pathogenesis has not been fully elucidated. Environmental factors play a k...

Structure-Informed Comprehensive Screening and Risk Prioritization of Organophosphorus Compounds in Human Serum: Revealing the Hidden Burden and Emerging Risks.

Organophosphorus compounds (OPCs) are widely distributed environmental contaminants for which there are increasing health concerns, yet human internal...

Integrating network toxicology, machine learning, and experimental evidence reveals candidate targets and pathways in PCDD/F-related colon cancer.

Previous studies have suggested that exposure to carcinogenic polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) pollutants...

Practical Integration of Bedside Real-Time Lung Ultrasound and Electrical Impedance Tomography for Guiding Personalized Perioperative Lung Protective Ventilation in Cardiothoracic and Vascular Anesthesia.

Patients undergoing cardiothoracic and vascular surgery are at uniquely high risk for postoperative pulmonary complications due to the confluence of s...

WGCNA and machine learning identify mitochondria programmed cell death-related genes as diagnostic biomarkers for septic shock.

Septic shock (SS), the most severe stage of sepsis, has a high mortality rate. Mitochondria-mediated programmed cell death (MPCD) plays a key role in ...

Artificial intelligence in orthopedic trauma surgery: a scoping review of current applications and research gaps.

Multiphysics learning with graph neural networks for thrombosis prediction in intracranial aneurysms.

Intracranial aneurysms are pathological dilations of cerebral arteries affecting 3-5% of the population. Their rupture can cause subarachnoid hemorrha...

Lower limb and feet wound image dataset.

A comprehensive wound-related image repository was developed to address critical gaps in existing medical imaging resources, particularly the lack of ...