Emergency Medicine

Reimagining Recovery: How Extended Ambulatory Models and Patient Hotels are Changing the Outpatient Surgical Paradigm.

Ambulatory extended recovery facilities, designed for 1-night stays, provide an alternative care setting for more complex patients and procedures than those treated in same-day surgery centers. Central to these programs are structured extended recovery after surgery pathways to promote rapid and safe recovery. Key considerations include patient and procedure selection, expanded on-site resources, ...

Multi-plane vision transformer for hemorrhage classification using axial and sagittal MRI data.

Identifying brain hemorrhages from magnetic resonance imaging (MRI) is a critical task for healthcare professionals. The diverse nature of MRI acquisitions with varying contrasts and orientation introduces complexity in identifying hemorrhage using neural networks. For acquisitions with varying orientations, traditional methods often involve resampling images to a fixed plane, which can lead to in...

DispatchMAS: fusing taxonomy and artificial intelligence agents for emergency medical services.

BACKGROUND: Emergency medical dispatch is a critical, high-stakes process where dispatcher decisions directly impact patient outcomes. While standardi...

A Pilot Study to Evaluate Artificial Intelligence-Driven Early Retrieval of Medical Histories in the Emergency Department.

STUDY OBJECTIVE: To assess the feasibility and acceptability of using ChatGPT to obtain histories of present illnesses directly from patients or careg...

Elucidating the mechanism of plasticizers associated with atherosclerosis through network toxicology, bulk RNA sequencing, and single-cell RNA sequencing analysis.

PURPOSE: This study aims to screen and identify potential atherosclerosis (AS) biomarkers associated with common plasticizer exposure, providing a bas...

Artificial intelligence and toxicology: Knowledge compared with other medical specialties.

Graph Machine Learning Can Estimate Drug Concentrations in Whole Blood from Forensic Screening Results.

LC-HRMS is widely used in forensic toxicology for broad-scope screening. When a newly emerging or rarely encountered compound is tentatively identifie...

Development and Validation of a Machine Learning Model to Predict Oral Anticoagulant Use in Stroke From Prothrombin Time-International Normalized Ratio and Activated Partial Thromboplastin Time.

BACKGROUND: Timely recognition of oral anticoagulant use is critical in acute stroke but is often hampered by impaired consciousness and unavailable m...

Approval of AI-Based Medical Devices in China From 2020 to 2025: Retrospective Analysis.

BACKGROUND: Artificial intelligence-based medical devices (AIMDs) have emerged as transformative technologies in modern health care. However, comprehe...

Practical adaptability of a pre-hospital prognostic prediction model for patients following out-of-hospital cardiac arrest during the COVID-19 pandemic.

OBJECTIVES: The overwhelmed situation under the COVID-19 pandemic has worsened the quality of emergency medical care and the mortality rate due to out...

ABC-YOLO: Automated skin burn depth classification using YOLO architectures.

Accurate classification of skin burn depth is vital for determining appropriate treatment and accelerating the healing process. This study conducts a ...

FAD-MIL: a weakly supervised fracture detection model based on X-ray images.

Fractures are among the most common presentations to emergency and orthopedic services, yet radiograph interpretation remains inconsistent, particular...

Nanotoxicology meets smart polymers: advancing safety-by-design nanomaterials.

Smart polymers have played great role in enhancing nanomedical application areas in drug delivery, diagnosis and environment sensitive treatment syste...

A computational model for predicting shock in patients at risk of urosepsis after urolithiasis surgery.

Influencing factors and predictive model for in-hospital complications in patients with spontaneous intracerebral hemorrhage: A single-center retrospective study.

Integration of detection and tracking networks for automated rib multiplanar reconstruction: a feasibility study for fracture diagnosis.

OBJECTIVE: Rib fractures are common yet time-consuming to diagnose. This study explores automation via multiplanar reconstruction and intelligent dete...

Diagnostic performance and confidence of an optimized deep-learning algorithm for the detection of intracranial hemorrhages.

OBJECTIVES: To evaluate the performance of an optimized deep-learning-based algorithm (AI) for the detection and subtyping of intracranial hemorrhage ...

Integrating Large Language Models Into Trauma Education for Medical Students: Randomized Controlled Pilot Trial.

BACKGROUND: The exponential growth of medical knowledge presents a paradox for modern medical education. While access to information is immediate, app...

Artificial intelligence in emergency skeletal X-ray: post-deployment monitoring and clinical impact of incorrect AI results.

OBJECTIVES: This study evaluates the impact of the artificial intelligence (AI) application BoneViewTM within a radiographer-supervised clinical workf...

A metabolomics-driven machine learning model for osteoporosis risk prediction.

Osteoporosis, marked by decreased bone mineral density (BMD), poses a major public health concern by increasing fracture risk, lowering quality of lif...