Emergency Medicine

New Model, Old Risks? Sociodemographic Bias and Adversarial Hallucinations Vulnerability in GPT-5

Extending our validated benchmarking work, GPT-5 showed no improvement in sociodemographic-linked decision variation compared with GPT-4o and seemed to be worse on several endpoints. We re-tested GPT-5 with a fixed pipeline: 500 physician-validated emergency vignettes, each replayed across 32 sociodemographic labels plus an unlabeled control, answering the same four questions (triage, further test...

A Deep Learning Framework for Automated Triage of Breast Cancer Biopsies in Malaysia: A Pragmatic Trial to Reduce Resource Consumption and Diagnostic Turnaround Time

Malaysia faces a significant burden of breast cancer, compounded by a chronic shortage of pathologists. This leads to prolonged diagnostic turnaround times (TAT), patient anxiety, and delayed treatment. Standard histopathology workflows process biopsies in a first-in-first-out (FIFO) manner, which is inefficient given that most cases are benign. This study aimed to develop and validate a deep lear...

Prompt Engineering Enables Open-Source LLMs to Match Proprietary Models in Diagnostic Accuracy for Annotation of Radiology Reports

The aim of this study was to test whether open-source Large Language Models (LLMs) can match the diagnostic accuracy of proprietary models in annotati...

Scalable screening for emergency department missed opportunities for diagnosis using sequential eTriggers and large language models

Missed opportunities for diagnosis (MODs), sometimes termed diagnostic errors, are a major cause of patient morbidity and mortality in the emergency d...

Childhood Maltreatment and Risk for Illicit Substance Use: Evidence for Mid-Adolescence as a Sensitive Exposure Period

Childhood maltreatment is a well-established risk factor for substance misuse. However, it remains unclear whether risk for specific illicit substance...

Protocol for Radiographer x AI led discharge

Emergency Department (ED) overcrowding, often exacerbated by prolonged patient length of stay (LOS), is a global challenge. Patients presenting with s...

Long-Term Carotid Plaque Progression and the Role of Intraplaque Hemorrhage: A Deep Learning-Based Analysis of Longitudinal Vessel Wall Imaging

Carotid atherosclerosis is a major contributor in the etiology of ischemic stroke. Although intraplaque hemorrhage (IPH) is known to increase stroke r...

SAHDAI-XAI Subarachnoid Hemorrhage Detection Artificial Intelligence- eXplainable AI: Testing explainability in SAH Imaging Data and AI Modeling

Subarachnoid hemorrhage (SAH) is a life-threatening and crucial neurological emergency. SAHDAI-XAI (Subarachnoid Hemorrhage Detection Artificial Intel...

Explainable Artificial Intelligence for Prognostic Stratification in Out-of-Hospital Cardiac Arrest Patients Undergoing Extracorporeal Cardiopulmonary Resuscitation

Prognostication in patient with out-of-hospital cardiac arrest (OHCA) underwent extracorporeal cardiopulmonary resuscitation (ECPR) remains challengin...

Mining medical narratives on geriatric falls to predict post-fall hospitalization via survival models and large language models

Timely admission to the emergency department is a crucial determinant of patient outcomes. Conversely, unnecessary hospital admissions can overburden ...

Artificial Intelligence-assisted reader evaluation in acute CT head interpretation (AI-REACT): a multireader multicase study

Non-contrast CT head scans (NCCTH) are the most frequently requested cross-sectional imaging in the Emergency Department. While AI tools have been dev...

Clinical Agents Don’t Care

Large language models (LLMs) now power clinical agents that can plan, call tools, and write into electronic health records (EHRs). They are becoming a...

Foundation model embeddings enable cardiovascular screening for people living with HIV in Vietnam using wearable signals

Cardiovascular disease (CVD) screening faces significant challenges in resource-limited settings, where infrastructure and computational constraints p...

Explainable Deep Learning for Lesion-Level Detection of Diabetic Retinopathy: A Segmentation Approach Using Fundus Images Graded as Mild-to-Moderate Nonproliferative Diabetic Retinopathy

Deep learning has shown promise in diabetic retinopathy screening using fundus images. However, many existing models operate as “black boxes,” providi...

From Concept to Code: AI- Powered CODE-ICH Transforming Acute Neurocritical Response for Hemorrhagic Strokes

Intracerebral hemorrhage (ICH) is among the most devastating forms of stroke, characterized by high early mortality and limited time-sensitive treatme...

The Cognitive Safety Net: Comparing Human and AI Diagnostic Reasoning during Complex Clinical Situations

Diagnostic error in high-stakes clinical environments remains a significant cause of preventable harm. While a new generation of customisable digital ...

Neural network-based identification of easily-obtainable demographic and clinical characteristics to identify people with tuberculosis

We consider the application of machine learning to the classification of tuberculosis (TB) based on clinical and demographic data. Such data is routin...

Deep learning on 3D ECG geometry predicts ischemia

Three-dimensional (3D) electrocardiography (ECG) is a recent methodological advance that extends the dimensionality of the standard ECG, enabling geom...

Evaluation of Large Language Models in the Clinical Management of Patients With Upper Gastrointestinal Bleeding : Insights From Real-World Patient Data

Upper gastrointestinal bleeding (UGIB) is a life-threatening emergency requiring rapid risk assessment. Current scoring tools have limited accuracy. L...

Synthetic Validation of Pediatric Trust Instruments using Persona-Driven Large Language Models

Trust is foundational to patient-physician relationships and is associated with improved care-seeking and adherence in primary care. However, validate...