# Artificial Intelligence to Reshape the Healthcare Ecosystem
## 2 AI Techniques for Healthcare  
The current state of the use of machine learning (ML) algorithms in healthcare is already quite complex [22]. In general, both supervised and unsupervised learning algorithms are used. The algorithms in the first class use tagged training data to configure  
Figure 1: General classification of AI techniques involved in healthcare. Figure 2: Graphical representation of the organization of the paper. the parameters of the algorithms, while unsupervised algorithms discover detailed information from untagged input data. Supervised algorithms, such as _decision trees_[22], are typically used for classification and regression tasks, while unsupervised ML is suitable for discovering patterns in data, such as for clustering tasks. In the following, we focus on the machine learning techniques that underpin much of the current state and progress of AI in healthcare. Some tools already incorporate AI techniques, such as QIIME2 [26] and Nextflow [27], that are widely used [28; 29; 30; 31; 32]. In the following, we focus on the AI techniques that are driving the major innovations in healthcare. A complete mathematical treatment of each of these and all the variants of each algorithm is beyond the scope of this paper. However, in addition to presenting the rationale for each technique, we also present basic mathematical modeling and provide the interested reader with directions for further study in the references. The techniques we discuss in this paper are those that refer to artificial neural networks (ANNs). These networks implement a computational mathematical model composed of a group of interconnections between nodes, which are vaguely inspired by the functioning of biological neurons. In order for a neural network to implement the desired mathematical function, it is necessary to train it to configure its parameters. These parameters make a neural network an adaptive system, that is, one that changes its non-linear structure (nodes and interconnections) of statistical data based on information that passes through it during the learning phase. A generic neural network includes three types of processing units, as shown in Figure 3.  
* I--Input nodes: These receive and process external data by adapting them to the internal nodes, to which each node I is connected;
* H--Hidden (internal) nodes: Organized in multiple levels, each of these nodes processes the received signals and transmits the result to the subsequent nodes. They actually perform, independently of each other, the data processing process through the use of algorithms;
* O--Output nodes: Final layer of nodes that collect the processing results of the H-layer and adapt them to the next neural network block. Each connection in a neural network is associated with a numerical trainable weight which controls the amount of information carried by the connection downstream. In addition, each node has a numerical bias associated with it. An activation function is used for taking in the input received by a node and processing it to obtain the desired output. Common non-linear activation functions used in ANNs include [33]:  
Figure 3: Basic structure of a multi-layer neural network. * **ReLU (rectified linear unit)**: \[\text{ReLU}(x)=\text{max}(0,x)\] (1) It is widely used due to both its simplicity and its effectiveness in mitigating the vanishing gradient problem. * **Leaky ReLU**: \[\text{Leaky ReLU}(x)=\text{max}(\alpha x,x)\] (2) where \(\alpha\) is a small constant that allows a small, non-zero gradient when the unit is inactive. It aims at mitigating the so-called _dying ReLU_ problem. * **Sigmoid**: \[\sigma(x)=\frac{1}{1+e^{-x}}\] (3) It is an S-shaped curve, that maps input to a range between 0 and 1. It is prone to vanishing gradients. * **Tanh (hyperbolic tangent)**: \[\text{tanh}(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}\] (4) It is an S-shaped curve that maps input to a range between \(-1\) and \(1\). It is also prone to vanishing gradients.