## 2 Artificial Intelligence in Healthcare  
**Machine learning (ML)** is the discipline that builds mathematical models and computer algorithms to perform specific tasks by learning patterns and inferences directly from data using computers, without being explicitly programmed to conduct these tasks [10]. ML algorithms can be either used for supervised learning, where the machine is provided with output labels to be associated with a set of input variables, or unsupervised learning. A popular supervised ML method is Support Vector Machines (SVM), which, by means of a kernel function, projects the data into a higher-dimensional feature space and determines a hyperplane in this feature space, which separates data points into categories [11]. Ensemble ML (EML) methods, such as Random forests or AdaBoost, are other supervised methods, which aggregate multiple learners, such as Decision Trees, into a single learner [12,13]. Naive-Bayesian (NB) classifier calculates the probability of each class using the Naive Bayes formula [14,15]. In unsupervised learning, the labels for given sets of input variables are not known, and the algorithm aims at finding correlations, patterns, or structures in the input variable space [16,17]. These include k-means clustering [18], principal component analysis (PCA) [19], Stochastic Neighbor Embedding (SNE) [20], and Laplacian eigenmaps [21].  
**Deep learning (DL)** is a group of methods, which can be employed for supervised or unsupervised learning on any type of data, image, or signal. DL employs models with multiple stacks of neural layers to learn inherent patterns from input data and generate comprehensive representations, in contrast to classical ML methods, which use hand-crafted features manually extracted as input [2].  
Nowadays, radiological and pathology images are stored, together with their reports, in picture archiving and communication systems (PACS). Besides, with the introduction of electronic health records (EHRs), systematic collections of patient health information have been made available, which include qualitative data, such as documents and records of patient demographics, medical records, and laboratory and diagnostics tests [22].  
ML and DL, if applied to this large and often unstructured digital content, can determine information useful for epidemiological, clinical, and research studies [23,24]. Natural language processing (NLP) techniques, a combination of AI and linguistics, aimed at developing a computer's ability to understand human language [25], can be used to extract clinically relevant information from pathology and radiology reports [26], which can be integrated with features extracted from digital radiologic and pathology images stored in PACS [27].  
The process used for these analyses is defined as \"Data Mining\". Data mining is used to find trends, patterns, correlations, anomalies, and features of interest in a database [28] in a data-driven inductive approach, which generates hypotheses from data [29]. Ideally, data mining necessitates the '4 V's' of 'Big Data'--volume, variety, velocity, and veracity of data. Instead of being used for prediction or diagnosis, in this case, ML is used to find clinically similar patients in the unstructured database, using all available multimodal clinical data available, with the aim of discovering important groupings or defining features in the data [28].  
Once similar patients are identified, the diagnosis, treatment, and outcome extracted from EHRs and other digital content can be ranked to give recommendations [17], e.g., by computerized clinical decision support systems (CDSS), which aid in decision-making [30]. In this way, pipelines can be designed to continuously and automatically extract information and improve the accuracy of patient outcome prediction [31].