Emergency Medicine

Artificial intelligence-driven predictive analytics for postoperative management and recovery in trauma patients.

PURPOSE OF REVIEW: Post-traumatic care is evolving from a reactive, protocol-driven paradigm to a predictive, personalized approach. This review examines how artificial intelligence and machine learning are redefining postoperative management by predicting complications before they manifest. RECENT FINDINGS: Recent literature (2023-2025) highlights three major advances: (a) the validation of gradi...

Commentary: Verify before trust: real-world application of an FDA-approved adult-trained artificial intelligence model for intracranial hemorrhage detection in children.

Use of Machine Learning to Predict Hospital Admission for EMS-Treated Infants After a Suspected BRUE.

OBJECTIVES: This study explored the use of different applied machine learning (ML) classification algorithms to predict hospital admission for infants...

Exploring the toxicological impact of N-nitrosodimethylamine (NDMA) exposure on bladder urothelial carcinoma (BLCA) through network toxicology, machine learning, and multi-dimensional bioinformatics analysis.

BACKGROUND: N-Nitrosodimethylamine (NDMA), classified as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC), is ubiquitous...

Hip fracture detection on radiographs using an artificial intelligence-based support tool: a diagnostic accuracy study.

BackgroundAssessment of subtle hip fractures on radiographs can be difficult, especially among less experienced emergency physicians, which may prolon...

Early Prediction of Adverse Stroke Outcomes using Non-clinical Factors and Missing Data: A Machine Learning Study.

Introduction Early prediction of stroke outcomes using prognostic tools may help clinical decision making and inform resource allocation. However, cli...

Prehospital Blood Collection for High Sensitivity Cardiac Troponin Measurement in Patients with Acute Chest Pain.

OBJECTIVES: High sensitivity cardiac troponin (hs-cTn) measures are used in the emergency department (ED) to evaluate patients with acute chest pain. ...

Evaluating GPT-4 Performance on Plastic Surgery Oral Examination Vignettes: A Quantitative and Qualitative Analysis.

BACKGROUND: The use of artificial intelligence (AI) in medicine is rapidly evolving. However, its role in plastic and reconstructive surgery remains u...

Prediction of the relationship between childhood trauma and psychological disturbances among younger adults using machine learning algorithms.

Prediction of Adverse Events in Single Ventricle Physiology Infants Using Artificial Intelligence Tools.

BACKGROUND: Adverse events (AEs) within the cardiac ICU (CICU) are associated with high mortality and comorbidity. Patients with single ventricle (SV)...

Early individualized risk prediction using clinical data for children during the febrile phase of dengue in outpatient settings in Vietnam and Thailand.

Dengue severity prediction models are usually developed using hospitalized patient data, but triage and hospital admission are mainly evaluated in out...

Perceptions and knowledge of machine learning for paediatric related decision support in emergency care - A UK and Ireland network survey study of clinician leaders.

This study explores clinician leaders understanding and perception at site level towards machine learning (ML) decision support tools for paediatric r...

Improving drug identification in overdose death surveillance by using clinical natural language processing models.

The rising rate of drug-related deaths in the United States, largely driven by fentanyl, requires timely and accurate surveillance. However, critical ...

An integrated study combining network toxicology machine learning and molecular simulation reveals the molecular mechanisms of permanent hair dyes in breast cancer.

Permanent hair dyes have been linked to an increased risk of breast cancer (BC), though the underlying mechanisms remain unclear. To address this know...

Current perspectives in cardiogenic shock.

Cardiogenic shock (CS) remains a leading cause of death in intensive cardiac care. Outcomes are limited by delayed recognition of hypoperfusion, heter...

Commercial AI platforms improve reproducibility but fail to meet clinical precision thresholds for intracranial aneurysm measurement: implications for serial surveillance.

BACKGROUND: Intracranial aneurysm affect 3-7% of the global population, with rupture causing > 80% of non-traumatic subarachnoid hemorrhage and approx...

Artificial intelligence in emergency surgery: a scoping review within the artificial intelligence in emergency and trauma surgery (ARIES) project.

AIM: To map and critically appraise the current literature on Artificial Intelligence (AI) applications in emergency general surgery, with a focus on ...

Artificial intelligence for fracture detection on computed tomography: a comprehensive systematic review and meta-analysis of diagnostic test accuracy in non-commercial and commercial solutions.

Rising patient volumes, the increasing use of computed tomography (CT) imaging in emergency departments and the resulting prolonged waiting times high...

Identifying genome-by-childhood trauma interactions for depression using a forest-based approach in the UK Biobank and Adolescent Brain Cognitive Development Study.

Depression is shaped by both genetic and environmental factors, but genome-wide interaction studies (GWIS) often lack power to detect complex gene-env...

Predicting recurrent cardiac arrest within one year after surviving In-Hospital Cardiac Arrest using a machine learning model.

BACKGROUND: Patients discharged alive after in-hospital cardiac arrest (IHCA) have an increased mortality up to a year after hospital discharge. Impro...