… mechanism for deploying and validating an AI-based system for detecting abnormalities on
chest X-… benchmark for deploying AI systems for chest radiograph analysis in clinical practice. …

… the time when patients met all ARDS criteria, the time from ARDS onset to CNN detection
was quantified to determine if potential delays might occur if the network was deployed in …

… on CXRs were trained and validated on the CheXpert [4,6,8] … framework to validate such a
system while being deployed at a … for deploying AI systems for chest radiograph analysis in …

… performance of algorithms can degrade significantly when deployed in clinical practice [24]. …
and validated the model’s diagnostic performance in a real-world setting before deploying …

… for various abnormalities and identifies, categorizes, and highlights suspicious regions of
interest (ROIs) using the deployed AI models. The AI models were trained on over 1.5 million …

… During the first wave of the pandemic, we deployed a previously developed and validated
deep-learning AI algorithm for assisted interpretation of chest radiographs for use by …

… be deployed. Because the model would be deployed on every adult ED chest radiograph, …
To simulate real-time performance, temporal validation included all adult chest radiographs …

… In this retrospective study, a deep learning model was trained for pneumothorax detection
by merging two large open-source chest radiograph datasets: ChestX-ray14 and CheXpert. It …

… We developed and validated deep learning models for chest radiograph interpretation by
using adjudicated labels as a rigorous reference standard and by using a clinically …

… Cross-validation assessment To further assess the pre-deployment durability of the original
LLIED model, 36 a fivefold cross-validation 64 was executed on tier 1 for LLIED detection in …