AIMC Topic: Emergency Service, Hospital
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Exploring the potential of artificial intelligence models for triage in the emergency department.
Postgraduate medicine
Oct 17, 2024
OBJECTIVE: To perform a comparative analysis of the three-level triage protocol conducted by triage nurses and emergency medicine doctors with the use of ChatGPT, Gemini, and Pi, which are recognized artificial intelligence (AI) models widely used in...
Diagnostic accuracy of artificial intelligence for identifying systolic and diastolic cardiac dysfunction in the emergency department.
The American journal of emergency medicine
Oct 15, 2024
INTRODUCTION: Cardiac point-of-care ultrasound (POCUS) can evaluate for systolic and diastolic dysfunction to inform care in the Emergency Department (ED). However, accurate assessment can be limited by user experience. Artificial intelligence (AI) h...
Spatial patterns of rural opioid-related hospital emergency department visits: A machine learning analysis.
Health & place
Oct 13, 2024
As opioid-related overdose emergency department visits continue to rise in the United States, there is a need to understand the location and magnitude of the crisis, especially in at-risk rural areas. We analyzed sets of ZIP code level electronic hea...
A large language model-based clinical decision support system for syncope recognition in the emergency department: A framework for clinical workflow integration.
European journal of internal medicine
Sep 28, 2024
Differentiation of syncope from transient loss of consciousness can be challenging in the emergency department (ED). Natural Language Processing (NLP) enables the analysis of free text in the electronic medical records (EMR). The present paper aimed ...
Impact of a deep learning-based brain CT interpretation algorithm on clinical decision-making for intracranial hemorrhage in the emergency department.
Scientific reports
Sep 27, 2024
Intracranial hemorrhage is a critical emergency that requires prompt and accurate diagnosis in the emergency department (ED). Deep learning technology can assist in interpreting non-enhanced brain CT scans, but its real-world impact on clinical decis...
Artificial Intelligence to Predict Billing Code Levels of Emergency Department Encounters.
Annals of emergency medicine
Sep 24, 2024
STUDY OBJECTIVE: To use artificial intelligence (AI) to predict billing code levels for emergency department (ED) encounters.
Acute cholecystitis diagnosis in the emergency department: an artificial intelligence-based approach.
Langenbeck's archives of surgery
Sep 24, 2024
OBJECTIVES: This study aimed to assess the diagnostic performance of a support vector machine (SVM) algorithm for acute cholecystitis and evaluate its effectiveness in accurately diagnosing this condition.
Quality assessment of expedited AI generated reformatted images for ED acquired CT abdomen and pelvis imaging.
Abdominal radiology (New York)
Sep 18, 2024
PURPOSE: Retrospectively compare image quality, radiologist diagnostic confidence, and time for images to reach PACS for contrast enhanced abdominopelvic CT examinations created on the scanner console by technologists versus those generated automatic...
Radiography, Abdominal
Pelvis
Radiographic Image Interpretation, Computer-Assisted
Quality Assurance, Health Care
Radiology Information Systems
Tomography, X-Ray Computed
Female
Aged
Adult
Male
Middle Aged
Contrast Media
Emergency Service, Hospital
Humans
Artificial Intelligence
Retrospective Studies
The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies.
Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
Sep 11, 2024
INTRODUCTION: Accurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence-based triage in the clinical ...
Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration.
Critical care explorations
Sep 11, 2024
BACKGROUND: Prediction-based strategies for physiologic deterioration offer the potential for earlier clinical interventions that improve patient outcomes. Current strategies are limited because they operate on inconsistent definitions of deteriorati...
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