Emergency Medicine

Left Atrial Stiffness Index for Refining Diastolic Stress Test Selection in Suspected Heart Failure With Preserved Ejection Fraction.

INTRODUCTION: Diagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging in patients with exertional dyspnea and inconclusive resting echocardiography. The left atrial stiffness index (LASI), calculated as E/e' divided by left atrial reservoir strain (LARS), reflects the relationship between estimated left ventricular filling pressure and left atrial compliance. OBJECTIV...

Prospective evaluation of a large language model clinical decision support system in the emergency department.

Prospective evidence for artificial intelligence (AI)-based clinical decision support in emergency departments remains limited. Here we conducted a DECIDE-AI stage 1 evaluation of SHAKED, a clinical decision support system built on multiple large language models, in a tertiary emergency department. Over 4 weeks, 1,138 patients were analyzed across two parallel units-one using SHAKED and one follow...

Machine Learning Models for Identifying Factors Associated With Workplace Violence Among Emergency Nurses: A Comparative Study.

OBJECTIVE: To develop and compare three machine learning models for identifying factors associated with workplace violence (WPV) among emergency depar...

Artificial Intelligence in Ultrasound Imaging: Opportunities for Improving Diagnostic Accuracy in Gulf Health Care.

Ultrasound imaging is widely used across cardiology, hepatology, obstetrics, breast and thyroid imaging, and emergency care because it is real-time, n...

Deep Learning of Lateral Thoracolumbar Radiographs and Clinical Risk Factors for Incident Vertebral Fracture: Single-Center Retrospective Cohort Study.

Early identification of patients at risk of incident vertebral fracture remains challenging because routine clinical risk assessment does not fully ca...

Cost-Effectiveness of an Artificial Intelligence as a Medical Device (AIaMD) for Triaging Patients Presenting to Primary Care with Concerns that They Have a Skin Cancer: A Modelling Study.

INTRODUCTION: Health services are struggling to cope with the growing numbers of people coming with skin lesions they are worried could be cancer. Usi...

A Multidisciplinary Team-Based Large Language Model Framework for Predicting Postoperative Neurological Complications in Acute Type A Aortic Dissection: Model Development and Validation Study.

BACKGROUND: Postoperative neurological complications (PNCs) after acute type A aortic dissection (ATAAD) surgery are clinically emergent and require m...

Generative Artificial Intelligence (AI)-Assisted Self-Diagnosis and the Patient-Physician Relationship: Mixed Methods Study of Calibration, Participation, and Trust.

BACKGROUND: Patients increasingly use generative AI to interpret symptoms and seek health information, yet limited evidence shows how AI-assisted self...

Development and Internal Validation of a Multimodal Prediction Model for Moderate-to-Severe Osteoporotic Vertebral Compression Fractures Using Bone Quality Biomarkers Derived From CT and MRI.

RATIONALE AND OBJECTIVES: To develop and internally validate interpretable prediction models using pre-fracture CT-derived Hounsfield unit (HU) values...

Unsupervised Single-Class Anomaly Detection in Rat Liver Using a Model of Normal Histomorphology.

An unsupervised, single-class anomaly detection approach based on a vision transformer architecture was developed to aid histopathology evaluation of ...

Graph-theoretic active learning for the closed-loop discovery of stochastic heterogeneous composites.

The inverse design of resilient infrastructure materials is hindered by the combinatorial complexity inherent in optimizing stochastic, heterogeneous ...

Anomaly detection as a tool for identifying cases of interest in postmortem toxicology.

Intoxication-induced death, both intentional and unintentional, is a global public health concern, contributing substantially to mortality in many reg...

A novel AI system for preliminary triage support in single-tooth edentulous spaces using intraoral images.

This study aimed to demonstrate the integration of deep learning (DL) and machine learning (ML) using only occlusal photographs to provide preliminary...

Comparison of a Computable Pediatric Sepsis Phenotype Tool in Argentinian and U.S. Cohorts.

OBJECTIVE: Sepsis is a leading cause of mortality in low- and middle-income countries. This study identifies computable pediatric sepsis phenotypes (P...

Are large language models such as ChatGPT, capable of supporting patients and general practitioners after spine surgery?

PURPOSE: To evaluate whether large language models (LLMs) can provide accurate, complete, and audience-adapted answers to common spine-surgery-related...

IShuffleNet-PCNN hybrid classifier within an MResU-Net-based framework for spinal cord injury detection and classification using CT images.

BACKGROUND: Spinal cord injury (SCI) is a serious medical condition. Spinal Cord Injury limits the movement of the body, blocks the nervous system and...

The role of artificial intelligence in predicting readmission risk in emergency patients: An umbrella review.

BACKGROUND: Hospital readmission following emergency care remains a persistent challenge, reflecting gaps in care continuity, discharge planning, and ...

Development and clinical validation of a point-of-care cytology screening tool for oral potentially malignant disorders and oral squamous cell carcinoma using a deep learning convolutional neural network: protocol for a diagnostic accuracy study.

INTRODUCTION: Oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) present a remarkable public health challenge worldw...

Application of Artificial Intelligence (AI) in cancer symptom management for adult cancer survivors: a scoping review.

BACKGROUND AND AIM: Artificial Intelligence (AI) has been increasingly used in cancer survivorship to support symptom management. This scoping review ...

CT-based prediction of hematoma expansion and adverse outcomes after intracerebral hemorrhage: evidence appraisal, artificial intelligence translation, and GeroScience perspectives.

Spontaneous intracerebral hemorrhage (ICH) is a highly lethal and disabling form of stroke, in which hematoma expansion (HE) is a major and potentiall...