Emergency Medicine

Asymmetric sociodemographic disparity in evidence-grounded clinical AI

AI-assisted clinical care may compound, rather than correct, existing health inequities. We applied Omar and colleagues' validated four-domain emergency-medicine benchmark to OpenEvidence (OE), a literature-grounded clinical LLM used by tens of thousands of US physicians daily, across 100 emergency-department cases and 20 sociodemographic labels. OE was consistent on the codified clinical decision...

Structured large language model extraction of clinical factors from electronic health record text supports scalable psychiatric severity prediction

Background: Mental health systems face escalating demand that exceeds clinician capacity, making accurate severity-based triage a critical bottleneck. Severity assessment guides treatment intensity, resource allocation, and risk management, yet most clinically relevant information remains embedded in unstructured electronic health record (EHR) narratives, limiting its utility for scalable decision...

Systematic toxicological study of PFOS/PFOA co-exposure driving prostate cancer: Core target identification, TME immune remodeling, and combination drug prediction

Background: Per- and polyfluoroalkyl substances (PFAS), particularly perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA), are persisten...

BioMADE: Predicting Torsades de Pointes from molecular structures through biologically informed representations

Drug-induced arrhythmias, particularly Torsades de Pointes (TdP), pose a significant risk to patient safety and can sometimes have life-threatening ou...

The Value of Mechanistic Priors in Sequential Decision Making

Hybrid mechanistic models, physical priors with learned residuals, promise to reduce the data required for good decisions, but have no computable crit...

Prompt-engineering improves clinical safety of large language models for opioid equipotency conversion

Background: Large language models (LLMs) are increasingly used in medical education and clinical decision-making, but their reliability in high-risk m...

Solving Emergency Department Triage with Small Language Models

Emergency department (ED) triage assigns patients a five-level Emergency Severity Index (ESI) score that determines care priority. We investigate the ...

Optimizing Screening for Intrauterine Fetal Growth Restriction in Low-Resource Settings Using 2D Ultrasound: A Deep Learning Approach

Severe fetal growth restriction (sFGR) affects 5 to 10% of pregnancies worldwide and is a major contributor to perinatal morbidity and mortality, part...

Transforming Patient Voices into Early Predictors of Survival Using Nonlinear Mixed-Effect Models and AI/ML for Patient-Centered Decision-Making

Patient-reported outcomes (PROs) capture the patient voice and have been associated with improved clinical outcomes in oncology, but their prognostic ...

Artificial Intelligence Agents in Mental Health: A Systematic Review and Meta Analysis

The rapid rise of large language models (LLMs) and foundation models has accelerated efforts to build artificial intelligence (AI) agents for mental h...

Differentiable latent structure discovery for interpretable forecasting in clinical time series

Background: Timely, uncertainty-aware forecasting from irregular electronic health records (EHR) can support critical-care decisions, yet most approac...

Domain-Adapted Small Language Models for Reliable Clinical Triage

Accurate and consistent Emergency Severity Index (ESI) assignment remains a persistent challenge in emergency departments, where highly variable free-...

Artificial-Intelligence-Enabled Early Malnutrition Risk Assessment Tools for Elderly Trauma Patients in Intensive Care Units

Background & Aims: Accurate assessment of clinical malnutrition using anthropometric and functional indicators could improve the care of elderly traum...

Multicohort development and validation of a machine learning model to predict six-month functional traumatic brain injury outcomes in a large national registry

Background: Prognostication after moderate-to-severe traumatic brain injury (TBI) rarely captures long-term functional recovery, despite its importanc...

Prognosis of stroke subtypes in whole population health systems data: a matched cohort study

Background Outcome after stroke varies according to stroke subtype by location, but healthcare systems data studies do not include subtyping informati...

Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs

Self-supervised learning in healthcare has largely relied on invariance-based objectives, which maximize similarity between different views of the sam...

Large language models and retrieval augmented generation for complex clinical codelists: evaluating performance and assessing failure modes

Objectives: Large language models (LLMs) have shown promise in creating clinical codelists for research purposes, a time-consuming task requiring expe...

Generalizing intensive care AI across time scales in resource-limited settings

Temporal resolution of physiological monitoring in intensive care varies widely across healthcare systems. Artificial intelligence models assume a uni...

No One Left Behind: Adaptive Tablet Modalities for Digitally Excluded Emergency Department Patients Design, Implementation, and Social Evidence for an Impairment-First Interface

Background: The urgent care departments in Europe face a structural paradox: accelerating digitalisation is accompanied by a patient population that i...

Learning Preference-Based Objectives from Clinical Narratives for Sequential Treatment Decision-Making

Designing reward functions remains a central challenge in reinforcement learning (RL) for healthcare, where outcomes are sparse, delayed, and difficul...