**1. Introduction**

A large part of diagnosis in various specialties of medical care relies heavily on image analysis. Depending on the type of technique used, more or less detail of the structures of interest can be obtained. It will also depend on the type of technique, whether the image is in two dimensions or if there are several slices that then can form a three-dimensional reconstruction [1]. Some specific techniques can also produce video output [2]. All of these different formats can be adapted for use as training and testing material for computer vision models. Computer vision, a subfield of artificial intelligence (AI), comprises all those techniques that allow a computer system to understand an image or a set of images and produce as a result a numerical or symbolic output. This output can be used to make a decision about the image [3]. When these models are applied to healthcare images, the output can be used to make a clinical decision [4]. Within computer vision algorithms, we have those that are handcrafted, where a person analyzes the set of images to be classified and chooses the features to be extracted from those images. For example, if we want to classify cardboard boxes in a scene, the person will probably choose to detect the edges of the box and the texture of the cardboard, as a first step [3]. Now, thanks to advances in research in this area, neural networks can also be used. A neural network is a computational algorithm composed of a set of interconnected nodes called artificial neurons, which are similar in function to neurons in the human nervous system. A neuron in this network receives information from the preceding neuron, processes it, and transmits it to other neurons. Networks can be simple, with very few layers of neurons, or complex, with many layers and many interconnections [5]. These models have the advantage that the determination of the features is automatic and does not need to be handcrafted. However, neural networks require a large amount of data to be able to perform accurate feature extraction with minimal error. In addition, as the number of connections between its different neurons is very high, it becomes complex to elucidate which features have been selected to produce an output from an initial image [3, 5, 6]. In the following chapter, we will discuss the most common AI applications of neural networks as computer vision models in the clinical medical field. In addition, we will analyze the different obstacles that the field of AI has encountered in its development along with the advancement that these vision applications have brought to the medical field.
