Emergency Medicine

A deep learning framework for recognizing skin changes secondary to chronic venous insufficiency in clinical photographs: a multicentre validation study.

BACKGROUND: Chronic venous insufficiency (CVI) produces heterogeneous lower-extremity skin changes that often mimic inflammatory dermatoses, complicating the differentiation between venous etiologies and conditions requiring dermatologic care. Coexisting venous signs further confound visual interpretation, leading to diagnostic variability. To address this unmet clinical need, we developed and ext...

Raman spectroscopic detection of salivary thiocyanate for smoking exposure stratification.

Distinguishing smokers from non-smokers and stratifying smokers based on their smoking history/exposure (years) are essential for evaluating the risk of tobacco-associated diseases and supporting early preventive care. It enhances the reliability of epidemiological research by examining tobacco exposure within populations. In forensic science and toxicology, detection of smoking biomarkers aids in...

Predicting Neonatal Respiratory Outcomes Using Machine Learning: A Systematic Review Through a Life Course Lens.

BACKGROUND: Neonatal respiratory outcomes remain leading drivers of neonatal intensive care unit (NICU) morbidity, mortality, and prolonged hospitaliz...

Portable RF brain scanner enables stroke type detection using deep learning.

Stroke remains a leading cause of mortality worldwide, demanding rapid, accurate diagnosis; distinguishing stroke types matters because treatments dif...

Combined Molecular Fingerprint and Descriptor Features Enable Classical Machine Learning to Match Deep Learning Performance Across the Tox21 Toxicity Panel.

BACKGROUND AND OBJECTIVE: Molecular fingerprints and physicochemical descriptors encode complementary structural information, yet most Tox21 benchmark...

Artificial Intelligence Cannot Replace Peer Reviewers but May Help Editors Triage: A Comparative Analysis of a Large Language Model and Human Reviewer Recommendations at the American Journal of Sports Medicine.

BACKGROUND: The peer review system faces increasing strain from rising manuscript volumes, reviewer fatigue, and well-documented interreviewer disagre...

Artificial intelligence in cardiopulmonary resuscitation: a systematic review.

Cardiac arrest remains a major cause of mortality and neurological disability, and its management depends on rapid recognition, effective resuscitatio...

Development and validation of interpretable machine learning models to predict 30-day mortality in patients with intracerebral hemorrhage.

Intracerebral hemorrhage (ICH) carries high early mortality. To enable personalized decision-making, we developed and validated interpretable machine ...

Prediction and mechanistic insights into drug-induced reproductive toxicity through integrated machine learning, FAERS-based signal comparison, and network toxicology analyses.

Drug-induced reproductive toxicity is a critical concern in drug safety evaluation, whereas conventional assessment methods are often constrained by h...

Skyer: a novel benchmark for evaluating the effectiveness of large language models in emergency department triage.

OBJECTIVES: Emergency department (ED) overcrowding causes diagnostic challenges, prolonged wait times, and impairs appropriate triage, often due to hu...

Unraveling Bone-Skin Crosstalk Enables miRNA Nanoformulation for Cutaneous Neurovascular Reconstruction in Diabetic Mice.

The clinical intractability of diabetic foot ulcers stems from a profound uncoupling of cutaneous neurovascular networks, rendering standard metabolic...

Integrating epigenetic and metabolic indicators for non-invasive endometrial cancer triage: a machine learning approach.

OBJECTIVE: Abnormal uterine bleeding (AUB) is a primary symptom indicative of endometrial cancer (EC), yet its diagnosis still primarily relies on inv...

Green radiology (part 1): environmental sustainability and energy consumption in medical imaging, with perspectives from Japan.

Climate change is an important public health challenge, and healthcare itself contributes to greenhouse gas emissions. Within healthcare, radiology is...

[Tele-emergency physicians-a cost-benefit analysis].

Telemedical applications are increasing-ranging from patients contacting a general practitioner to tele-emergency medicine and tele-intensive care for...

Artificial Intelligence for Evidence Synthesis of Emerging Biologics to Improve Skeletal Health in Osteogenesis Imperfecta: Systematic Review and Meta-Analysis.

BACKGROUND: Osteogenesis imperfecta (OI) is a rare genetic disorder characterized by bone fragility and recurrent fractures. Emerging biologics demons...

Predicting antenatal care dropout using machine learning and describing maternal and neonatal outcomes by dropout status in Sidama Region, Ethiopia: a retrospective cohort study.

OBJECTIVES: We aimed to develop and validate machine-learning models to predict antenatal care (ANC) dropout and describe maternal and neonatal outcom...

Simulation of covariate and concept drift in machine learning hospital admission prediction from emergency triage.

Machine learning models that predict hospital admission at triage may support patient flow forecasting, yet the effects of covariate drift, concept dr...

A robust, high-content NAM for repeatable and predictive developmental and reproductive toxicity assessment in C. elegans.

Developmental and reproductive toxicity (DART) assessment is essential for product safety evaluation but relies heavily on vertebrate models that are ...

Radiomics-based machine learning for splenic injury diagnosis using computed tomography (CT) images.

Efficient trauma assessment is essential for optimal patient care, with imaging playing a critical role in the detection of injuries. Rapid and accura...

Health system learning enables generalist neuroimaging models.

Frontier artificial intelligence (AI) models have advanced rapidly through training on internet-scale public data, yet such systems lack access to pri...