Emergency Medicine

Semi-supervised Learning for Generalizable Intracranial Hemorrhage Detection and Segmentation.

Purpose To develop and evaluate a semi-supervised learning model for intracranial hemorrhage detecti...

Estimation of racial and language disparities in pediatric emergency department triage using statistical modeling and natural language processing.

OBJECTIVES: The study aims to assess racial and language disparities in pediatric emergency departme...

[Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].

OBJECTIVE: To construct and validate the best predictive model for 28-day death risk in patients wit...

A comprehensive exploration of artificial intelligence in orthopaedics within lower-middle-income countries: a narrative review.

Integrating Artificial Intelligence (AI) in orthopaedic within lower-middle-income countries (LMICs)...

Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.

PURPOSE: Adverse effects of chemotherapy often require hospital admissions or treatment management. ...

Can artificial intelligence help the emergency physician diagnose poisoning?

Artificial intelligence and emergency services: We need to take a step forward.

APPLICATION OF ARTIFICIAL INTELLIGENCE IN CIVIL AND MILITARY MEDICINE.

Artificial intelligence (AI) encompasses the advancement of computers and robots, enabling them to s...

Improved Vertebral Fracture Assessment: The Game-Changing Potential of Deep Learning with Multidetector CT.

RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population.

PURPOSE: To evaluate the diagnostic performance of a robotically aligned optical coherence tomograph...

Comparison of the problem-solving performance of ChatGPT-3.5, ChatGPT-4, Bing Chat, and Bard for the Korean emergency medicine board examination question bank.

Large language models (LLMs) have been deployed in diverse fields, and the potential for their appli...

AI for Detection of Tuberculosis: Implications for Global Health.

Tuberculosis, which primarily affects developing countries, remains a significant global health conc...

Deep learning for acute rib fracture detection in CT data: a systematic review and meta-analysis.

OBJECTIVES: To review studies on deep learning (DL) models for classification, detection, and segmen...

[Real-time traking and virtual reality technology-assisted trauma orthopaedic surgery robot for femoral neck fractures].

OBJECTIVE: To investigate the effectiveness of real-time tracking and virtual reality technology（RTV...

[Orthopaedic robot assisted closed reduction and cannulated screw internal fixation for the treatment of femoral neck fractures].

OBJECTIVE: To investigate the preliminary clinical effect of closed reduction and cannulated nail in...

Deep Learning for Midfacial Fracture Detection in CT Images.

This study deploys the deep learning-based object detection algorithms to detect midfacial fractures...

Utilization of Machine Learning Approaches to Predict Mortality in Pediatric Warzone Casualties.

BACKGROUND: Identification of pediatric trauma patients at the highest risk for death may promote op...

Research on workflow recognition for liver rupture repair surgery.

Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for ca...

Development of a Flexible Chain of Thought Framework for Automated Routing of Patient Portal Messages.

The increase in utilization of patient portal messages has imposed a considerable burden on healthca...

Towards Optimizing LLM Use in Healthcare: Identifying Patient Questions in MyChart Messages.

The volume of patient-provider messages is on the rise, and Large Language Models (LLMs) can potenti...

Integrating Remote Patient Monitoring Data into Machine Learning Models for Predicting Emergency Department Utilization.

The integration of Remote Patient Monitoring (RPM) data into risk stratification models has emerged ...