Emergency Medicine

Accuracy of Chatbot-Based Health Advice Related to Traumatic Dental Injuries: A Systematic Review and Meta-Analysis.

BACKGROUND/AIMS: The growing use of generative artificial intelligence, especially chatbots, has motivated researchers to test the accuracy of health advice provided. Though there are studies comparing chatbot health advice for traumatic dental injuries (TDI), variable results have been reported. Hence, this systematic review aimed to assess contemporary chatbots for their ability to accurately an...

Diagnostic decision-making, vertebral augmentation, and osteoporosis therapy in vertebral compression fractures: a narrative review.

Vertebral compression fractures (VCFs) represent the most prevalent osteoporotic fracture and constitute a growing cause of morbidity, mortality, and healthcare utilization worldwide. Although often considered benign, VCFs are associated with chronic pain, progressive spinal deformity, functional impairment, decreased pulmonary capacity, and increased short- and long-term mortality, especially in ...

The nephrologist in the present and near future: between algorithms and autonomy.

This perspective examines how artificial intelligence (AI) may reshape nephrology over the next two decades while keeping the nephrologist's role cent...

Assessing Different Machine Learning Algorithms to Identify Traumatic Cardiac Arrest.

OBJECTIVE: Traumatic cardiac arrest differs from non-traumatic regarding epidemiology. This study evaluated five machine learning classifiers' ability...

Critical Care-Specific vs General-Purpose Large Language Models in Emergency Intensive Care Unit Diagnosis: Single-Center Retrospective Paired Comparative Study.

BACKGROUND: The emergency intensive care unit (EICU) manages the most critically ill patients, where rapid and accurate diagnosis is essential yet cha...

Multicenter Validation of an AI-Based CTA Tool for Anterior Circulation Large Vessel Occlusion Detection.

BACKGROUND: Rapid identification of anterior circulation large vessel occlusions (LVOs) is critical for timely mechanical thrombectomy in acute ischem...

AI-assisted software for chronic wound detection: Development, validation, and certification strategy.

AIMS: This study evaluates the feasibility of an artificial intelligence (AI)-assisted software tool for early identification and classification of ch...

Integrating New Approach Methodologies and Artificial Intelligence to Advance Central Nervous System Toxicity Prediction: Lessons from Preclinical and Clinical Case Studies.

Central nervous system (CNS) toxicities remain a major cause of drug attrition and represent a persistent challenge in predicting neurological risk du...

From decay to discovery: A new antimicrobial peptide from Chrysomya megacephala (Diptera: Calliphoridae) larvae.

The larvae of Chrysomya megacephala (Diptera: Calliphoridae) thrive in environments rich in decaying organic matter and dead animals, which are often ...

Multimodal artificial intelligence for prostate cancer imaging: workflow-relevant fusion of mpMRI, PSMA PET, ultrasound, and clinical data for diagnosis, local staging, and treatment personalization.

BACKGROUND: Prostate cancer imaging is inherently multimodal, yet many AI tools remain single-modality and therefore misaligned with real-world abdomi...

Optimising Pharmacovigilance Efficiency with MLIT (Machine Learning for Intelligent Triage): A Tool for Statistical Safety Alerts.

BACKGROUND AND AIM: Pharmacovigilance is essential to ensuring patient safety by enabling timely identification of adverse reactions in increasingly c...

Predicting Prehospital Blood Transfusion After Motor Vehicle Trauma.

OBJECTIVES: Prehospital blood transfusion (PHBT) improves outcomes among patients with traumatic hemorrhagic shock, yet the epidemiology and geographi...

Using Natural Language Processing to Identify Adverse Drug Events Characterized by Medication Replacement in Primary Care Electronic Medical Records: Algorithm and Validation Study.

BACKGROUND: Health care systems generate vast amounts of unstructured text, such as clinical notes, which capture nuanced patient experiences, clinica...

KA-TMoE: A Deep Learning Method Using Time-Series CT Radiomics to Predict Post-Radiotherapy Rib Fractures in NSCLC Patients.

BACKGROUND AND PURPOSE: Rib fracture is a recognized clinical complication in medically inoperable patients with non-small cell lung cancer (NSCLC) un...

Large language models in respiratory care: safety and clinical integration.

BACKGROUND: Large language models (LLMs) are rapidly entering respiratory medicine workflows. Their clinical role remains unclear. A central concern i...

Myocarditis in the Modern Era: Navigating Diagnosis, Innovative Treatments, the COVID-19 Challenge and the Role of Artificial Intelligence.

Myocarditis is an inflammatory disease of the myocardium with diverse aetiologies, ranging from infections and autoimmune processes to drug-induced re...

Sociodemographic bias in LLMs' clinical decision-making for dizziness.

ObjectiveAs large language models (LLMs) enter clinical decision support, concerns persist about sociodemographic bias. We assessed whether LLM recomm...

From bedside queries to complex care: AI for decision-making in infectious diseases.

BACKGROUND: Infectious Diseases require rapid decisions based on heterogeneous and evolving clinical data. At the same time, the shortage of infectiou...

Correction to: Angioarchitectural features of hemorrhagic presentation of brain arteriovenous malformations: A multivariate and explainable machine learning study.

An interpretable multimodal biomechanical-radiological model for predicting fixation failure in osteoporotic hip fractures: A retrospective cohort study.

This study aimed to develop and validate a multimodal prediction model integrating biomechanical and radiological variables to predict internal fixati...