Emergency Medicine

An Efficient and Interpretable Foundation Model for Retinal Image Analysis in Disease Diagnosis

Artificial intelligence (AI) foundation models for colour fundus photography (CFP) have been extensively studied and demonstrated great potential for advancing ocular and systemic health screening. However, their high computational demands and limited clinical interpretability constrain real-world clinical application. These models rely on self-supervised learning with massive unlabeled datasets t...

CT-based Osteoporosis Classification and Bone-Muscle Interaction Mapping Using Multiple Interpretable Machine Learning Models with the BMINet Framework

Osteoporosis progresses through stages characterized by declining bone mineral density, vertebral deterioration, and muscle atrophy, with bone-muscle interactions driving synergistic degeneration. This study retrospectively collected data from 444 patients aged 50 and older, who underwent DXA, CT, and MRI scans at the First Affiliated Hospital of Soochow University. CT values were measured for 6 v...

Grounded large language models for diagnostic prediction in real-world emergency department settings

Emergency departments face increasing pressures from staff shortages, patient surges, and administrative burdens. While large language models (LLMs) s...

AI for Mortality Prediction from Head Trauma Narratives

Head injuries are a leading global cause of mortality and disability, highlighting the critical need for advanced prognostic tools to inform clinical ...

Detecting papilloedema as a marker of raised intracranial pressure using artificial intelligence: a systematic review

Automated detection of papilloedema using artificial intelligence (AI) and retinal images acquired through an ophthalmoscope for triage of patients wi...

Leveraging artificial Intelligence and online psychotherapy to achieve efficient and coordinated services within a healthcare setting: A quality improvement initiative

This study aimed to implement an artificial intelligence-assisted psychiatric triage program, assessing its impact on efficiency and resource optimiza...

Life Events Extraction From Healthcare Notes for Veteran Acute Suicide Prediction

Predictive models of suicide risk have focused on predictors extracted from structured data found in electronic health records (EHR), with limited con...

Designing a Substance Misuse Data Dashboard for Overdose Fatality Review Teams

Overdose Fatality Review (OFR) is a public health process in which cases of fatal overdose are carefully reviewed to identify prevention strategies. C...

Development and Validation of an Artificial Intelligence Predictive Model to Accelerate Antibiotic Therapy for Critical Ill Children with Sepsis in the Pediatric ED with Pediatric ICU Disposition

Pediatric sepsis accounts for over 72,000 US hospitalizations annually with significant mortality and morbidity. Many pediatric hospitals struggle to ...

Leveraging Large Language Models to Develop an Interpretable Prediction Model for Postpartum Hemorrhage Prior to the Onset of Labor

To evaluate whether large language models (LLMs) applied to prenatal clinical notes can predict postpartum hemorrhage (PPH) prior to the onset of labo...

Machine Learning in Psychiatric Health Records: A Gold Standard Approach to Trauma Annotation

Psychiatric electronic health records present unique challenges for machine learning due to their unstructured, complex, and variable nature. This stu...

Perceptions and Insights: A Qualitative Assessment of an AI-Assisted Psychiatric Triage System Implemented in an Outpatient Hospital Setting

The Canadian healthcare system is approaching a breaking point. With mental health being a leading cause of disability, innovative solutions are neces...

Concurrent prediction of in-hospital mortality and length of stay using single-task, multi-class, and multi-task machine learning

Accurate predictions of discharge timing and in-hospital mortality could improve hospital efficiency, but clinician estimates are often inconsistent a...

Impact of Iron Deficiency on Clinical Outcomes in Congestive Heart Failure: A Retrospective Analysis of Risk Stratification and Mortality

Iron deficiency frequently coexists with congestive heart failure, thereby increasing morbidity and mortality. Although guidelines typically define ir...

Spine age estimation using deep learning in lateral spine radiographs and DXA VFA to predict incident fracture and mortality

Spine age estimated from lateral spine radiographs and DXA vertebral fracture assessments (VFAs) could be associated with fracture and mortality risk....

Evaluating AI Reasoning Models in Pediatric Medicine: A Comparative Analysis of o3-mini and o3-mini-high

Artificial intelligence (AI) is increasingly playing a crucial role in modern medicine, particularly in clinical decision support. This study compares...

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 betwe...

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-dem...

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...