IMPORTANCE: Triage in the emergency department (ED) is a complex clinical judgment based on the tacit understanding of the patient's likelihood of survival, availability of medical resources, and local practices. Although a scoring tool could be valu...
AJR. American journal of roentgenology
Jul 28, 2021
Of over 100 FDA-cleared artificial intelligence (AI) tools for triage, detection, or diagnosis in medical imaging, only one is cleared for use in children. Thus, children may be unable to benefit from the advances that AI provides to adults. Furtherm...
BACKGROUND: Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea. For rapid triage, we studied machine learning-based triage systems composed ...
Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invas...
In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individu...
Artificial intelligence-driven voice technology deployed on mobile phones and smart speakers has the potential to improve patient management and organizational workflow. Voice chatbots have been already implemented in health care-leveraging innovativ...
AIM: To evaluate the role that artificial intelligence (AI) could play in assisting radiologists as the first reader of chest radiographs (CXRs), to increase the accuracy and efficiency of lung cancer diagnosis by flagging positive cases before passi...
Computer methods and programs in biomedicine
May 2, 2021
BACKGROUND AND OBJECTIVES: The multiple chest x-ray datasets released in the last years have ground-truth labels intended for different computer vision tasks, suggesting that performance in automated chest x-ray interpretation might improve by using ...