… metric was designed to reward or penalize algorithms, … This metric provided high scores
for early sepsis predictions to … However, the common framework that traditional metrics …

… framework for all stakeholders in the endoscopy AI ecosystem regarding the standards,
metrics… We provide a framework for the metrics used for AI algorithm development during internal …

… metric of a particular algorithm can quickly see that metric and compare that algorithm with
other algorithms. … positives and negatives; the remaining metrics can be calculated from these …

… forecasting metrics but also incorporates other metrics developed by PHM researchers.
The proposed classification framework for PHM performance metrics is shown in Figure 1. …

… and error metrics for regression and classification algorithms, … metrics (PFEMs).
Performance and error measures are vital elements in the process of evaluating ML models/frameworks. …

… prevent algorithms from being fully trusted. In this article, we review the major regulatory
frameworks … AI algorithms. We identify the following major shortcomings of the current regulatory …

… The success of current artificial intelligence (AI) approaches … a framework for the healthier
use of metrics that includes: … “error” of our algorithms that explicitly links metrics in the use of AI. …

… , artificial intelligence (AI) methods and AI-enabled metrics … algorithms. A few studies
evaluated the effectiveness of providing metrics and other feedback derived from machine learning …

… metric is a measure that allows one to obtain the numerical value of some property of the
algorithm, … To evaluate the methods of explainable artificial intelligence, a variety of metrics from …

… Performance aspects refer to metrics such as computational cost which can be highly
expensive or even stopping the execution of the algorithm for very large datasets. Model …