Emergency Medicine

Profile of deaths mentioning ischemic and hemorrhagic stroke in Brazil: a population-based machine learning analysis

Brazil has the highest stroke rates in Latin America. The aim of this study was to investigate the profile of deaths mentioning stroke in Brazil between 2000 and 2019 and the relationship between multiple causes of death and stroke subtype. Deaths mentioning stroke and other conditions were identified using individual death records from the country’s mortality information system (SIM). Strokes wer...

Socio-Demographic Modifiers Shape Large Language Models’ Ethical Decisions

Large language models’ (LLMs) alignment with ethical standards is unclear. We tested whether LLMs shift medical ethical decisions when given socio-demographic cues. We created 100 clinical scenarios, each posing a yes/no choice between two conflicting ethical principles. Nine LLMs were tested with and without 53 socio-demographic modifiers. Each scenario-modifier combination was repeated 10 times ...

High Sensitivity in Spontaneous Intracranial Hemorrhage Detection from Emergency Head CT Scans Using Meta-Learning Approach

Spontaneous intracranial hemorrhages have a high disease burden. Due to increasing medical imaging, new technological solutions for assisting in image...

SleepDepNet: A Multi-Task Transformer Framework for Assessing Sleep Quality and Depression Risk from Social Media Narratives

The bidirectional relationship between sleep disturbances and depression presents a serious challenge for digital mental health research and intervent...

PanEcho: Complete AI-enabled echocardiography interpretation with multi-task deep learning

Echocardiography is a cornerstone of cardiovascular care but relies on expert interpretation and manual reporting from a series of videos. We propose ...

The use of Artificial Intelligence in the out of hospital care settings: A Scoping Review

Out of hospital services face significant challenges, including growing patient demand, workforce limitations, and evolving care pathways. Artificial ...

The gSOS Polygenic Score is Associated with Bone Density and Fracture Risk in Childhood

The polygenic risk score genetic quantitative ultrasound speed of sound (gSOS) was developed using machine learning algorithms in adults of European a...

Evaluation synthesis analysis can be accelerated through text mining, searching, and highlighting: A case-study on data extraction from 631 UNICEF evaluation reports

UNICEF works to protect children’s rights and improve their well-being by partnering with governments and communities through various programs. These ...

Comparing Machine Learning and Nurse Predictions for Hospital Admissions in a Multisite Emergency Care System

Emergency department (ED) crowding strains patient care and drives up costs. Early decisions on the need for patient hospital admissions can allow for...

Understanding the Feasibility of Computer Vision in Diagnosing Respiratory Infections in Pediatric Emergency Rooms

Respiratory infections are a leading cause of pediatric emergency visits globally, requiring timely and accurate assessment. This study evaluated the ...

TrialGenie: Empowering Clinical Trial Design with Agentic Intelligence and Real World Data

Clinical trial design (CTD) is a time-consuming process that requires substantial domain expertise. Large-scale real-world data (RWD), such as electro...

Creation of an Open-Access Lung Ultrasound Image Database For Deep Learning and Neural Network Applications

Lung ultrasound (LUS) offers advantages over traditional imaging for diagnosing pulmonary conditions, with superior accuracy compared to chest X-ray a...

Radiomics-Based Early Triage of Prostate Cancer: A Multicenter Study from the CHAIMELEON Project

Prostate cancer (PCa) is the most commonly diagnosed malignancy in men worldwide. Accurate triage of patients based on tumor aggressiveness and stagin...

Identifying Cardiogenic Shock Sub-Phenotypes with Machine Learning: A Multicenter Study Combining Clinical and Echocardiographic Data

Sub-phenotyping cardiogenic shock (CS) patients using non-traditional clustering methods represents a step toward precision medicine, potentially impr...

ChatGPT Is Still Not Good Enough at Giving Care-Seeking Advice, or Is It?

Artificial Intelligence tools like ChatGPT are increasingly used by patients to support their care-seeking decisions, although the accuracy of newer m...

AI-MI: A Deep Learning Model to Predict Actionable Acute Coronary Syndrome Using 12-Lead ECGs

Chest pain is among the most common chief complaints in Emergency Departments (EDs), and differentiating acute coronary syndrome from low-risk chest p...

Machine Learning-based Mortality Prediction for Pediatric Fulminant Myocarditis Using Cytokine Profiles

Fulminant myocarditis (FM) is a rare but life-threatening pediatric condition that rapidly progresses to cardiogenic shock and fatal arrhythmias. Earl...

A comparative analysis of dengue, chikungunya, and Zika in a pediatric cohort over 18 years

Dengue, chikungunya, and Zika are diseases of major human concern. Differential diagnosis is complicated in children and adolescents by their overlapp...

Leveraging AI and Transfer Learning to Enhance Outcome Prediction for Out-of-Hospital Cardiac Arrest in Diverse Settings: Insights from the Pan-Asian Resuscitation Outcomes Study

Access to trustworthy artificial intelligence (AI) models for clinical applications like emergency care is unevenly distributed globally due to health...

AI Implementation in U.S. Healthcare and Its Association With Elder Mortality and Quality of Care

Hospitals are increasingly adopting artificial intelligence (AI) tools in clinical care. However, their overall impact on the health of older adults r...