Emergency Medicine

Mapping the knowledge landscape of planarian regeneration: a century of bibliometric insights.

BACKGROUND: Despite over two centuries of research, the knowledge landscape of planarian regeneration-a pivotal model for stem cell biology and regenerative medicine-remains fragmented, hindering interdisciplinary integration and translational progress. To address this gap, we conducted the first large-scale bibliometric analysis integrating machine learning-enhanced burst detection, hierarchical ...

Real-world multimetric comparison of four commercial artificial intelligence solutions for intracranial hemorrhage detection.

PURPOSE: To evaluate the real-world multimetric performance of four commercially available computed tomography (CT)-based artificial intelligence (AI) solutions for acute intracranial hemorrhage (AIH). METHODS: Patients who underwent non-contrast brain CT for suspected AIH in our emergency room between February and March 2024 were screened. After applying the inclusion and exclusion criteria, 436 ...

Computer-Vision Approach to Triaging Patient-Submitted Photos of Intestinal Ostomies.

BACKGROUND: Surgeons and ostomy nurses receive a high volume of stoma photos from patients. OBJECTIVE: This study aimed to develop and validate an aut...

Automated quality assurance for emergency department documentation: pilot comparison with physician peer review of simulated chest pain cases.

OBJECTIVES: To evaluate the feasibility and reliability of an artificial intelligence-driven quality assurance system for emergency chest pain documen...

Differentiating septic arthritis from non-infectious inflammatory causes of acute monoarticular arthritis in children: A machine learning approach based on routine laboratory tests.

OBJECTIVE: Acute monoarthritis in children poses a diagnostic challenge, particularly in distinguishing septic arthritis from non-infectious inflammat...

Machine learning-supported cross-excitation laser-induced fluorescence lidar for the classification of oil spill.

Accurate identification of the spilled oil type was crucial for implementing suitable emergency response measures and guiding further marine oil pollu...

Enhancing the golden hour: classification of traumatic brain injury, severity, and concomitant clinical phenotypes using prehospital continuous physiological data during air transport.

During emergency transport, clinical assessment and vital signs may lack the sensitivity to identify traumatic brain injury (TBI) and identify specifi...

An explainable real-time artificial intelligence -assisted system to reduce intradialytic hypotension.

BACKGROUND: Intradialytic hypotension (IDH) is a frequent complication in hemodialysis and is associated with adverse cardiovascular and neurological ...

Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department.

BACKGROUND: Pulmonary thromboembolism (PTE) is a life‑threatening condition that requires prompt and accurate evaluation in the emergency department (...

Improving Surgical Scheduling Efficiency with Operating Room Coordination and Allocation at Centre Antoine Lacassagne: A Comparative Analysis of Patient Outcomes and Operational Metrics.

BACKGROUND: Operating room (OR) scheduling is critical for timely patient care, optimal resource usage, and equitable surgical access. Despite the cos...

Development and validation of a routine blood test-based model to predict in-hospital postoperative pulmonary infection in older patients with hip fracture.

BACKGROUND: Postoperative pulmonary infection (PPI) is a common and serious complication in older adults undergoing hip fracture surgery, leading to p...

Enhancing Emergency Medical Response Education through Generative AI-Powered Game-Based Learning: A Retrospective Comparative Study.

OBJECTIVE: Traditional lecture-based learning (LBL) is often insufficient for cultivating the practical decision-making skills required in high-stakes...

Machine learning-based prediction of hospital-associated complications after tibial fracture surgery in older patients: a nationwide Japanese database study.

BACKGROUND: Older adults requiring emergency surgery for acute tibial fractures are vulnerable to hospital-associated complications (HACs), but admiss...

The Rise of AI Companions: From Prediction to Surgery in the Digital Twin Era.

Artificial intelligence (AI) is rapidly reshaping orthopaedic surgery, supported by advances in data science, computational power, and perioperative d...

Development of Convolutional Neural Networks for Classification and Characterisation of Proximal Humerus Fractures on Computed Tomography.

BACKGROUND: Agreement between surgeons on classification, characterization and choice of treatment for proximal humerus fractures (PHFs) is poor, lead...

Comprehensive analysis of the association between perfluorooctanoic acid exposure and osteosarcoma progression.

Perfluorooctanoic acid (PFOA), a persistent environmental pollutant, represents a chronic environmental stressor, yet its role in osteosarcoma progres...

Predictive Models for Time to First Opioid Use Disorder or Opioid Overdose Among Older Adults.

BACKGROUND: Limited data exist on predictive models incorporating patient-reported and claims-based measures to identify older adults at risk for opio...

Enhancing out-of-hospital emergency care via lexical machine learning modeling of chief complaints.

Emergency medical services (EMS) professionals make high-stakes decisions in austere environments. To support out-of-hospital emergency care, we devel...

Screening of core targets for Di(2-ethylhexyl) Phthalate-related gastric cancer based on machine learning, molecular docking, and SHAP analysis.

PURPOSE: Given the existing uncertainties regarding the link between Di(2-ethylhexyl) phthalate (DEHP) exposure and gastric cancer (GC) progression, t...

"Check Symptoms & Get Care": Mount Sinai's AI Triage Solution.

Mount Sinai Health System (MSHS), one of New York City's largest academic medical centers, faced a common patient-access challenge: individuals presen...