Emergency Medicine

Generative artificial intelligence provides accurate case selection in veterinary retrospective studies.

OBJECTIVE: To evaluate the agreement of automation tools with expert evaluators in identifying cases meeting inclusion and exclusion criteria for retrospective veterinary studies. METHODS: The review of medical records took place from December 16, 2024, through July 2, 2025. Medical records from 3 study populations (100 trauma dogs, 86 stent patients, and 100 cholecystectomy dogs) were assessed by...

Modern integrative prostate cancer diagnostics.

PURPOSE OF REVIEW: To review contemporary applications, performance, and implementation challenges of artificial intelligence (AI) in the radiological and pathological diagnosis of prostate cancer, and to highlight emerging multimodal AI biomarkers for prognosis and treatment selection. RECENT FINDINGS: In radiology, large multicenter studies demonstrate that MRI-based AI can detect clinically sig...

Computer-vision based recognition of cervical spine stabilization during trauma resuscitation.

BACKGROUND: Cervical spine (c-spine) injuries can lead to significant disability and mortality. Although stabilization is the primary management for s...

Quantitative EEG enhances early assessment and prognostic stratification of brain dysfunction in infants with abusive head trauma.

AIM: To assess the value of quantitative EEG (qEEG) as a diagnostic and prognostic biomarker in infants with abusive head trauma (AHT). Despite its ce...

Deep learning for pediatric femoral neck fracture detection in a multicenter study.

Pediatric femoral neck fractures (FNFs) are uncommon but may result in severe complications if undiagnosed. This study developed a deep learning model...

Persistent dysmorphology and dysfunction of the brain microvasculature following repeated trauma.

Mild traumatic brain injury typically produces no abnormalities on neuroimaging yet elicits symptoms that, in an increasing fraction of survivors, lin...

Deep Learning in Vertebral Fracture Detection: Systematic Review and Meta-analysis of Subject- vs. Vertebra-Level Approaches.

RATIONALE AND OBJECTIVES: To provide a context-aware evaluation of deep learning algorithms for vertebral fracture detection by disentangling subject-...

Wildfire smoke and dengue burden in Brazil: Evidence from a nationwide study.

Wildfire fine particulate matter (PM2.5) is an emerging health concern, yet its effects on mosquito-borne diseases, particularly dengue, remain unclea...

Artificial Intelligence in Prostate MRI: Addressing Current Limitations Through Emerging Technologies.

Prostate MRI has transformed lesion detection and risk stratification in prostate cancer, but its impact is constrained by the high cost of the exam, ...

Precision medicine in Moyamoya vasculopathy.

PURPOSE OF REVIEW: Moyamoya vasculopathy is a progressive cerebrovascular steno-occlusive disease with variable presentation. As revascularization tec...

Creating Virtual Stroke Networks: Current and Future Role of Artificial Intelligence, Mobile Imaging Applications, and Telehealth in Triage and Treatment of Acute Ischemic Stroke: A Scientific Statement From the American Heart Association: The American Academy of Neurology affirms the value of this statement as an educational tool for neurologists.

Rapid evaluation, triage, and transport of patients with stroke for thrombolytics, thrombectomy, and other acute treatments have become a vital part o...

When used for veterinary triage, artificial intelligence models recognise emergencies but are more likely than veterinary staff to flag non-urgent cases as urgent.

BACKGROUND: This study assesses the capability of ChatGPT and nurses in accurately triaging emergency patients compared to veterinarians. METHODS: Ret...

Bot vs. doc-who is better at reading proximal humerus fracture x-rays?

BACKGROUND: Artificial intelligence is becoming increasingly utilized as a source of convenient, efficient, and cost-effective information. Considerin...

Enhancing identification confidence in non-targeted screening of emerging contaminants via an ensemble retention time prediction model: Applications in screening and ecological risk assessment.

The increasing emerging contaminants (ECs) pose significant challenges to non-targeted screening (NTS) and annotation. Machine learning-based retentio...

Enhancing vertebral fracture prediction using multitask deep learning computed tomography imaging of bone and muscle.

OBJECTIVES: To develop and externally validate a computed tomography (CT)-based multitask learning model to predict fracture risk. MATERIALS AND METHO...

Development of an explainable machine learning model to reproduce and interpret expert pharmacological decisions in osteoporosis treatment.

The management of osteoporosis in real-world clinical practice is highly heterogeneous, reflecting the complexity and variability inherent in therapeu...

Wildfire-sourced PM2.5 and cause-specific emergency department visits in Victoria, Australia from 2014 to 2019.

Wildfires-sourced (WS) fine particulate matter (PM2.5) is known to adversely impact human health, while evidence regarding the burden of emergency dep...

Performance Comparison of a Domain-Specific Chatbot and General-Purpose Chatbots in Dental Traumatology.

BACKGROUND: Use of artificial intelligence chatbots in dental traumatology has increased. However, concerns regarding their reliability are yet to be ...

Artificial Intelligence in Pediatric Surgery: From Diagnostics and Preoperative Planning to Risk Stratification: A Comprehensive Review of Current Applications.

OBJECTIVE: Artificial intelligence (AI) is increasingly explored in pediatric surgical care, yet its translation into diagnostics and preoperative pla...

Secretary or SecretarAI: assessing the triage performance of AI vs human staff in a specialized dental clinic.

OBJECTIVE: Effective patient triage in specialized clinics is crucial for managing long waiting lists and mitigating clinical risk. This is often perf...