AI Medical Compendium Topic

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Emergency Service, Hospital

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Applications of Artificial Intelligence and Machine Learning in Emergency Medicine Triage - A Systematic Review.

Medical archives (Sarajevo, Bosnia and Herzegovina)
BACKGROUND: Overcrowding in Emergency departments adversely impacts efficiency, patient outcomes, and resource allocation. Accurate triage systems are essential for prioritizing care and optimizing resources. While traditional methods provide a found...

Comparison of a Novel Machine Learning-Based Clinical Query Platform With Traditional Guideline Searches for Hospital Emergencies: Prospective Pilot Study of User Experience and Time Efficiency.

JMIR human factors
BACKGROUND: Emergency and acute medicine doctors require easily accessible evidence-based information to safely manage a wide range of clinical presentations. The inability to find evidence-based local guidelines on the trust's intranet leads to info...

Performance of machine learning models in predicting difficult laryngoscopy in the emergency department: a single-centre retrospective study comparing with conventional regression method.

BMC emergency medicine
BACKGROUND: Emergency endotracheal intubation is a critical skill for managing airway emergencies in the emergency department (ED). Accurate prediction of difficult laryngoscopy is essential for improving first-attempt success, minimizing complicatio...

Automated identification of incidental hepatic steatosis on Emergency Department imaging using large language models.

Hepatology communications
BACKGROUND: Hepatic steatosis is a precursor to more severe liver disease, increasing morbidity and mortality risks. In the Emergency Department, routine abdominal imaging often reveals incidental hepatic steatosis that goes undiagnosed due to the ac...

AI-driven triage in emergency departments: A review of benefits, challenges, and future directions.

International journal of medical informatics
BACKGROUND: Emergency Departments (EDs) are critical in providing immediate care, often under pressure from overcrowding, resource constraints, and variability in patient prioritization. Traditional triage systems, while structured, rely on subjectiv...

An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records.

BMC medical informatics and decision making
BACKGROUND: Identifying key variables is essential for developing clinical outcome prediction models based on high-dimensional electronic medical records (EMR). However, despite the abundance of feature selection (FS) methods available, challenges re...

The diagnostic performance of automatic B-lines detection for evaluating pulmonary edema in the emergency department among novice point-of-care ultrasound practitioners.

Emergency radiology
PURPOSE: B-lines in lung ultrasound have been a critical clue for detecting pulmonary edema. However, distinguishing B-lines from other artifacts is a challenge, especially for novice point of care ultrasound (POCUS) practitioners. This study aimed t...

Establishing methodological standards for the development of artificial intelligence-based Clinical Decision Support in emergency medicine.

CJEM
OBJECTIVE: Artificial intelligence (AI) offers opportunities for managing the complexities of clinical care in the emergency department (ED), and Clinical Decision Support has been identified as a priority application. However, there is a lack of pub...

Artificial intelligence-based clinical decision support in the emergency department: A scoping review.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
OBJECTIVE: Artificial intelligence (AI)-based clinical decision support (CDS) has the potential to augment high-stakes clinical decisions in the emergency department (ED). However, its current usage and translation to implementation remains poorly un...