Journal of the American Medical Informatics Association : JAMIA
Apr 3, 2024
OBJECTIVES: The study aims to assess racial and language disparities in pediatric emergency department (ED) triage using analytical techniques and provide insights into the extent and nature of the disparities in the ED setting.
Medical archives (Sarajevo, Bosnia and Herzegovina)
Jan 1, 2024
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...
INTRODUCTION: This study examines triage judgments in emergency settings and compares the outcomes of artificial intelligence models for healthcare professionals. It discusses the disparities in precision rates between subjective evaluations by healt...
Background Breast screening enables early detection of cancers; however, most women have normal mammograms, resulting in repetitive and resource-intensive reading tasks. Purpose To investigate if deep learning (DL) algorithms can be used to triage ma...
Background Most artificial intelligence algorithms that interpret chest radiographs are restricted to an image from a single time point. However, in clinical practice, multiple radiographs are used for longitudinal follow-up, especially in intensive ...
Background Artificial intelligence (AI) algorithms have shown high accuracy for detection of pulmonary embolism (PE) on CT pulmonary angiography (CTPA) studies in academic studies. Purpose To determine whether use of an AI triage system to detect PE ...
Journal of public health (Oxford, England)
Aug 28, 2023
BACKGROUND: Intelligent artificial agents ('agents') have emerged in various domains of human society (healthcare, legal, social). Since using intelligent agents can lead to biases, a common proposed solution is to keep the human in the loop. Will th...
The journal of trauma and acute care surgery
Jun 1, 2023
The entry of Artificial Intelligence (AI) into Intensive Care has become a reality. We think that only with robust validation studies accomplished by a multidisciplinary team the gap between clinical research and clinical practice can be bridged.
BACKGROUND: Undertriaged patients have worse outcomes than appropriately triaged patients. Machine learning provides better triage prediction than conventional triage in emergency departments, but no machine learning-based undertriage prediction mode...