Emergency Medicine

Plasticizer mixture exposure-induced preeclampsia pathogenesis: Integrated single-cell and bulk transcriptomics, network toxicology, and molecular docking insights.

PURPOSE: While plasticizers are known preeclampsia (PE) risk factors, the mechanisms of combined exposure to ATBC, DEP, DMP, and DOP remain unclear. This study aims to elucidate their biological targets and pathogenic mechanisms in PE. METHODS: PE/toxin gene targets were predicted from databases. Biomarkers were identified via differential expression analysis, machine learning, expression verifica...

The micro- and nanoplastics exposome feedback loop drives synergistic air-lung toxicity.

Airborne micro- and nanoplastics (MNPs) are now recognized as persistent components of the atmospheric exposome. While their presence is established, their toxicological role remains incompletely understood. In this perspective, we propose the MNP Exposome Feedback Loop-a hypothesized self-reinforcing system in which MNPs act not as passive pollutants, but as active vectors and catalytic amplifier...

A clinical-AI correlation for integrating artificial intelligence into stroke care: a systematized literature review and practice framework.

BACKGROUND AND PURPOSE: The rapid integration of artificial intelligence (AI) into stroke care has outpaced many clinicians' ability to critically eva...

Beyond diagnosis: deep-learning-based analysis of hospitalization using abdominal radiographs in the emergency department.

PURPOSE: Abdominal radiography (AXR) is routinely performed in emergency departments (ED) but has limited clinical utility. Thus, this study developed...

Artificial intelligence-driven prostate cancer diagnosis: Enhancing accuracy and personalizing patient care.

Prostate cancer remains a major global burden; diagnostic pathways rely on prostate-specific antigen (PSA), multiparametric magnetic resonance imaging...

LI-RADS-aligned artificial intelligence for liver cancer diagnosis: methods, evidence, and clinical readiness.

Liver tumor diagnosis relies heavily on imaging, and the liver imaging reporting and data system (LI-RADS) provides a structured framework for evaluat...

Novel Psychoactive Substances: Slaying the Dragon With Artificial Intelligence.

BACKGROUND: The emergence of novel psychoactive substances (NPSs) has overwhelmed forensic, health care, and regulatory systems. Conventional analytic...

Increased Utilization of Telemedical Emergency and Nonurgent Care Following Deployment of Virtual Triage and Care Referral in Australia.

OBJECTIVE: To evaluate whether an artificial intelligence-based national virtual triage and care referral (VTCR) service in Australia improved care ac...

Acceptability of Using Artificial Intelligence in the National Health Service Breast Screening Program: A Randomized Online Survey of Screening-Eligible Women in England.

OBJECTIVE: To compare acceptability of 2 artificial intelligence (AI) use cases in the English National Health Servic Breast Screening Program. PATIEN...

An explainable predictive machine learning model reveals ARRB2 as a key gene in post-traumatic stress disorder: A GEO database study.

A number of processes and pathways have been reported in the development of Post-Traumatic Stress Disorder (PTSD), however, novel biomarkers need to b...

Development and Validation of an Automated Pipeline for the Detection of Monteggia Fracture Dislocations in Pediatric Radiographs.

BACKGROUND: Monteggia fractures are a complex elbow injury that can be missed in up to 50% of pediatric elbow injuries during initial radiographic ass...

Decipher the molecular network of PFOA in inflammatory bowel disease through integrating machine learning, molecular docking strategies, and validation in vitro.

OBJECTIVE: Perfluorooctanoic Acid (PFOA), widely recognized as an enduring environmental pollutant, is associated with immune system disruption and po...

Efficacy and comparative performance of machine learning models for stroke risk prediction in hypertensive patients: A systematic review and meta-analysis.

BACKGROUND: Stroke poses a significant health burden among hypertensive patients, where traditional risk models often lack precision. Machine learning...

Evaluating an Artificial Intelligence software for opportunistic low bone mineral density and osteoporosis screening: a validation study.

Despite the profound prevalence and fracture risk of osteoporosis, access to the gold standard DXA scans remains limited, especially in rural communit...

Generative artificial intelligence as a source of advice on resuscitation and first aid for laypeople: A scoping review.

INTRODUCTION: The performance of cutting-edge generative artificial intelligence (GenAI) in guiding laypeople on how to give help in health emergencie...

Predictive modeling of hospital emergency department demand using artificial intelligence: A systematic review.

BACKGROUND: Accurately forecasting patient arrivals in hospital emergency departments (EDs) is critical for hospital capacity and planning and clinica...

Aspartame promotes ovarian cancer progression: A multi-omics study integrating mendelian randomization, network toxicology, and in vitro validation.

The causal relationship between the artificial sweetener aspartame and ovarian cancer (OC), a highly lethal malignancy, remains unclear. This study, t...

Performance of ChatGPT versus spine surgeons as an emergency department spine call consultant.

BACKGROUND: Large language models (LLMs) like ChatGPT are increasingly being recognized as credible tools for use across diverse healthcare settings. ...

Industry 6.0 capabilities in orthopaedics: Towards hyper-personalized and autonomous surgical care.

BACKGROUND: Industry 6.0 represents the next frontier in technological evolution, integrating artificial intelligence (AI), autonomous robotics, digit...

Diagnosis of shoulder dislocation on AP radiographs: A comparative analysis of diagnostic performance between orthopedic surgeons, emergency physicians, and ChatGPT models.

OBJECTIVE: This study aimed to evaluate the diagnostic performance of ChatGPT in identifying acute shoulder dislocations and to compare its accuracy w...