###### Abstract
The radiomics paradigm, also known as quantitative imaging, is supported by AI-based image analysis tools and entails viewing biomedical images as sets of numerical data rather than as simple images [26; 27]. Machine learning (ML) is a subfield of AI, aiming at the development and tuning of learning algorithms that exhibit human-like learning properties for data analysis but are superior in performance, robustness, and velocity. Different ML approaches use a large set of models to predict a quantity of interest (or higher-level parameters) that satisfies some predefined requirements, starting from a sample dataset [25; 28; 29]. By feeding these algorithms with enough data, they can extract knowledge from the data. After the training phase, the ML model should be validated and tested for its reliability in a different dataset: this is, in simple terms, the so-called radiomic workflow [30]. ML includes four main approaches: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning; each approach involves different levels of data pre-treatment, algorithmic strategies applied to map the relationship between data, and the problems that can be solved. The first three methods are the most widely applied in radiology [31]. The simplest type of ML, supervised learning, is suitable for very general classification tasks. To be trained, this type of ML requires a data set in which each input (independent variable) is labeled according to the corresponding output [32; 33]. These algorithms gradually tune their performance while increasing exposure to samples during the training phase, thus learning to infer the relationship between data according to a mapping hypothesis (a model). During testing, the model associates one of the pre-defined class labels with fresh, unlabeled targets. When the actual labels are compared to the ones that were assigned, the algorithm's performance can be evaluated. Supervised learning is the basis of many AI applications to predict the risk of cancer, survival, nodule detection, and classification [34]. Unsupervised learning algorithms require the machine to discover a hidden structure within data, or at least a subset of data, where no labels are provided. Unsupervised learning can be applied to a variety of data mining (extracting information from data) challenges, including clustering tasks (which aim to divide the dataset into groups based on particular feature characteristics) and association tasks (which aim to identify association rules within the dataset) [28]. Between supervised and unsupervised learning is semi-supervised learning. This method primarily uses unlabeled data, with a small amount of labeled data also included. As a result, this type of ML addresses the issue of low data availability by using a large amount of readily available but untagged data (such as undiagnosed images) to train precise classifiers [35]. Potential relationships between or within data can be inferred using a wide range of models, each with its applicable design for data representation, analysis, and loss function minimization (a function that describes the approximation toward the desired performance) [36]. In very general terms, models may be statistically based, or artificial-neural-networks-based; both models are required to be computationally feasible (that could be translated into a programming code for being automated by a machine) [37]. Each model can be viewed as a different tactic when thinking about the various ML approaches (such as supervised learning) as different strategies to find significant patterns in data. In supervised learning, for example, these models represent various hypotheses for mapping the connection between input and output; the difference between the prediction and the observed data essentially determines the fitness of the hypothesis. Logistic regression, decision trees, and support vector machines are a few examples of statistical models used in supervised learning.