Table 1.

Important AI-related terms and definitions.

Term	Description	References
Machine learning (ML)	Process by which an algorithm encodes statistical regularities from a database of examples into parameter weights for future predictions	[11]
Deep learning (DL)	Multilayered complex ML platform comprised of numerous computational layers able to make accurate predictions	[6]
Supervised learning	Training an ML algorithm using previously labeled training examples, consisting of inputs and desired outputs provided by an expert	[7,11]
Unsupervised learning	When an ML algorithm discovers hidden patterns or data groupings without the need for human intervention	[11]
Reinforcement learning	Learning strategies towards acting optimally in certain situations with respect to a given criterion; such an algorithm obtains feedback on its performance by comparison with this criterion through reward values during training	[7]
Model	A trained ML algorithm that can make predictions from unseen data	[11]
Training	Feeding an ML algorithm with examples from a training dataset towards deriving useful parameters for future predictions	[11]
Features	Components of a dataset describing the characteristics of the studied observations	[1]
Decision tree	Nonparametric supervised learning method visualized as a graph representing the choices and their outcomes in the form of a tree; each tree consists of nodes (attributes in the group to be classified) and branches (values that a node can take)	[12,13]
Random forest	Ensemble classification technique that uses “parallel ensembling”, fitting several decision tree classifiers in parallel on dataset subsamples	[13]
Naïve Bayes (NB)	Classification technique assuming independence among predictors (i.e., assumes that the presence of a feature in the class is unrelated to the presence of any other feature)	[12]
Logistic regression	Algorithm using a logistic function to estimate probabilities that can overfit high-dimensional datasets, being suitable for datasets that can be linearly separated	[13]
K-nearest neighbors (KNN)	“Instance-based learning” or a non-generalizing learning algorithm that does not focus on constructing a general internal model but, rather, stores all instances corresponding to the training data in an n-dimensional space and classifies new data points based on similarity measures	[13]
Support vector machine (SVM)	Supervised learning model that can efficiently perform linear and nonlinear classifications, implicitly mapping their inputs into high-dimensional feature spaces	[12]
Boosting	Family of algorithms converting weak learners (i.e., classifiers) to strong learners (i.e., classifiers that are arbitrarily well-correlated with the true classification) towards decreasing the bias and variance	[12]
Artificial neural network (ANN)	An ML technique that processes information in an architecture comprising many layers (“neurons”), each inter-neuronal connection extracting the desired parameters incrementally from the training data	[6,11]
Deep neural network (DNN)	A DL architecture with multiple layers between the input and output layers	[11]
Convolutional neural network (CNN)	A class of DNN displaying connectivity patterns similar to the connectivity patterns and image processing in the visual cortex	[11]