Emergency Medicine

Empowering clinical trial design with agentic intelligence and real-world data.

Clinical trial design (CTD) is a time-consuming process that requires substantial domain expertise. Large-scale real-world data (RWD), such as electronic health records (EHR), encodes practice-based evidence that is of tremendous value to CTD. In recent years, many machine learning methods have been developed to extract such real-world evidence (RWE) from the RWD to inform CTD, but they still need...

An X-ray Dataset and Benchmark for AI-Based Diagnosis of Monteggia Fractures.

Monteggia fractures exhibit a high missed diagnosis rate of over 20%, largely attributable to their subtle radiographic presentation and frequently low clinical suspicion. While AI-based diagnostic methods hold considerable potential to enhance detection accuracy, their development has been hampered by the absence of a dedicated, well-annotated imaging dataset. To address this gap, we introduce MF...

Pediatric fracture classification in plain radiographs using EfficientNetV2 with proximal policy optimization fine-tuning.

Pediatric fracture detection in plain radiographs presents distinct clinical challenges due to the presence of growth plates, incomplete ossification,...

Computational Toxicology Prioritization of CYP3A4 and DPP7 as Candidate Triclosan-Relevant Molecular Targets in Ulcerative Colitis.

Triclosan (TCS), a synthetic broad-spectrum antimicrobial classified as a novel persistent organic pollutant, is detected in over 75% of human urine s...

A multicenter machine learning model for predicting ICU mortality in mechanically ventilated patients: development and external validation.

BACKGROUND: Accurate early prediction of mortality in mechanically ventilated intensive care unit (ICU) patients remains challenging due to disease he...

Performance of large language models on undergraduate endodontic multiple-choice questions.

OBJECTIVE: This study aimed to evaluate the accuracy and consistency of responses provided by three large language models (LLMs), ChatGPT-5.2, Gemini-...

Censoring chemical data to mitigate dual use risk.

Machine learning models have dual use potential, potentially serving both beneficial and malicious purposes. The development of open-source models in ...

Optical Navigation Robot-Assisted versus Conventional CT-Guided Localization of Pulmonary Nodules: A Comparison of Efficacy and Analysis of Complication Predictors.

PURPOSE: To compare the efficacy and safety of optical navigation robot-assisted versus conventional CT-guided preoperative localization of pulmonary ...

Prioritization of molecular signatures between BDE-209-relevant targets and ulcerative colitis: a network toxicology and bioinformatics analysis.

BACKGROUND: Decabromodiphenyl ether (BDE-209) is a widely used flame retardant and persistent environmental contaminant. However, the overlap between ...

Comparative Efficacy of Trained Versus Untrained Generative Artificial Intelligence Platforms in Providing Case-Based Multiple-Choice Questions on Traumatic Dental Injuries in the Pediatric Dentistry Curriculum: A Cross-Sectional Study.

BACKGROUND/AIM: To determine the comparative efficacy of trained versus untrained generative artificial intelligence platforms in providing multiple-c...

Performance evaluation of deep learning models for image analysis: Considerations for visual assessment and statistical metrics.

Deep learning-based automated image analysis (DL-AIA) has been shown to outperform trained pathologists in tasks related to feature quantification. Re...

Head and Neck Free Flap Reconstruction: Current Landscape and Emerging Technologies.

OBJECTIVE: To synthesize contemporary developments in head and neck oncologic free flap reconstruction, with emphasis on perioperative physiologic opt...

Efficacy evaluation of artificial intelligence in radiological imaging diagnosis based on randomized controlled trials: a scoping review.

OBJECTIVE: Artificial intelligence (AI) demonstrates significant potential in medical imaging diagnosis, yet its real-world clinical value requires va...

Operationalizing AI-Assisted Polypharmacy Review in Post-Acute and Long-Term Care: A 4Ms-Based, NP-Led Workflow.

Polypharmacy in post-acute and long-term care (PA/LTC) is common and is associated with falls, delirium, hospitalization, functional decline, and mort...

Machine learning-assisted risk assessment for fluoroquinolone treatment in Chryseobacterium indologenes bacteremia: A comparative study of model performance and clinical calibration.

INTRODUCTION: Chryseobacterium indologenes bacteremia poses significant therapeutic challenges due to intrinsic multidrug resistance and the absence o...

Implementation of an ai-enabled multimodal emergency care system is associated with improved sudden cardiac death rescue outcomes in anyang.

Sudden Cardiac Death (SCD) remains a leading cause of mortality worldwide, with outcomes critically dependent on the effective implementation of the "...

DeepTriage-CN: integrating clinical text with vital signs for emergency department admission prediction in an aging population.

Emergency department (ED) triage of older adults is challenging because standard early warning scores are often insensitive to atypical presentations....

Hierarchical multi-instance learning for targeted triage and classification of common left-to-right shunt lesions from echocardiography videos.

BACKGROUND: Transthoracic echocardiography is the first-line test for congenital heart disease (CHD), but accurate targeted triage and lesion subtypin...

Network toxicology and multi-omics identify potential interactions between between air pollutants and interferon-related signaling in tuberculosis.

BACKGROUND: Air pollution increases tuberculosis (TB) susceptibility, yet the underlying molecular mechanisms remain elusive. METHODS: We integrated h...

Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study.

BACKGROUND: Irregularly sampled data, as a common data structure in the medical field, is frequently observed in emergency clinical datasets. It poses...