Emergency Medicine

Machine Learning and Deep Learning Models for Automated Protocoling of Emergency Brain MRI Using Text from Clinical Referrals.

Purpose To develop and evaluate machine learning and deep learning-based models for automated protocoling of emergency brain MRI scans based on clinical referral text. Materials and Methods In this single-institution, retrospective study of 1953 emergency brain MRI referrals from January 2016 to January 2019, two neuroradiologists labeled the imaging protocol and use of contrast agent as the refer...

Effects of chronic exposure to biomass pollutants on cardiorespiratory responses and the occurrence of exercise-induced bronchoconstriction in healthy men.

Exposure to charcoal biomass (CB) pollutants affects the cardiorespiratory system. We assessed cardiopulmonary responses (CPR) to exercise in charcoal producers (CPs) compared to farmers and evaluated the prevalence of exercise-induced bronchoconstriction (EIB). Forty-five CPs and 36 farmers, healthy males aged 23-39, completed a 15-m Incremental Shuttle Walk and Run Test (15-m ISWRT). Air quality...

Artificial Intelligence in Spine Imaging: A Paradigm Shift in Diagnosis and Care.

Recent advancements in artificial intelligence (AI) can significantly improve radiologists' workflow, improving efficiency and diagnostic accuracy. Cu...

Inside a Metastatic Fracture: Molecular Bases and New Potential Therapeutic Targets.

INTRODUCTION: Bone metastases and pathological fractures significantly impact the prognosis and quality of life in cancer patients. However, clinical ...

Predicting Agitation Events in the Emergency Department Through Artificial Intelligence.

IMPORTANCE: Agitation events are increasing in emergency departments (EDs), exacerbating safety risks for patients and clinicians. A wide range of cli...

TRUST: An LLM-Based Dialogue System for Trauma Understanding and Structured Assessments

Objectives: While Large Language Models (LLMs) have been widely used to assist clinicians and support patients, no existing work has explored dialog...

VR-FuseNet: A Fusion of Heterogeneous Fundus Data and Explainable Deep Network for Diabetic Retinopathy Classification

Diabetic retinopathy is a severe eye condition caused by diabetes where the retinal blood vessels get damaged and can lead to vision loss and blindn...

Jekyll-and-Hyde Tipping Point in an AI's Behavior

Trust in AI is undermined by the fact that there is no science that predicts -- or that can explain to the public -- when an LLM's output (e.g. Chat...

Building Trust in Healthcare with Privacy Techniques: Blockchain in the Cloud

This study introduces a cutting-edge architecture developed for the NewbornTime project, which uses advanced AI to analyze video data at birth and d...

Assessing the Impact of External and Internal Factors on Emergency Department Overcrowding

Study Objective: To analyze the factors influencing Emergency Department (ED) overcrowding by examining the impacts of operational, environmental, a...

AI Ethics and Social Norms: Exploring ChatGPT's Capabilities From What to How

Using LLMs in healthcare, Computer-Supported Cooperative Work, and Social Computing requires the examination of ethical and social norms to ensure s...

Advanced Segmentation of Diabetic Retinopathy Lesions Using DeepLabv3+

To improve the segmentation of diabetic retinopathy lesions (microaneurysms, hemorrhages, exudates, and soft exudates), we implemented a binary segm...

Investigating LLMs in Clinical Triage: Promising Capabilities, Persistent Intersectional Biases

Large Language Models (LLMs) have shown promise in clinical decision support, yet their application to triage remains underexplored. We systematical...

ICGM-FRAX: Iterative Cross Graph Matching for Hip Fracture Risk Assessment using Dual-energy X-ray Absorptiometry Images

Hip fractures represent a major health concern, particularly among the elderly, often leading decreased mobility and increased mortality. Early and ...

Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects

Heterogeneous treatment effect estimation in high-stakes applications demands models that simultaneously optimize precision, interpretability, and c...

"Can't believe I'm crying over an anime girl": Public Parasocial Grieving and Coping Towards VTuber Graduation and Termination

Despite the significant increase in popularity of Virtual YouTubers (VTubers), research on the unique dynamics of viewer-VTuber parasocial relations...

Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversations

The advancement of AI systems for mental health support is hindered by limited access to therapeutic conversation data, particularly for trauma trea...

Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media

Drug overdose remains a critical global health issue, often driven by misuse of opioids, painkillers, and psychiatric medications. Traditional resea...

Leveraging Machine Learning Models to Predict the Outcome of Digital Medical Triage Interviews

Many existing digital triage systems are questionnaire-based, guiding patients to appropriate care levels based on information (e.g., symptoms, medi...

A Category-Fragment Segmentation Framework for Pelvic Fracture Segmentation in X-ray Images

Pelvic fractures, often caused by high-impact trauma, frequently require surgical intervention. Imaging techniques such as CT and 2D X-ray imaging a...