ABC | Volume 114, Nº4, April 2020

Marques et al. Artificial Intelligence in Cardiology Arq Bras Cardiol. 2020; 114(4):718-725 Review Article Figure 1 – Evolution of the number of works relating (Artificial Intelligence or Machine Learning) and Cardiology. Source: Pubmed.Accessed on 12/15/2018. Mesh Words: Cardiology and Machine Learning. 160 140 120 100 80 60 40 20 0 1986 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 y = 1.5442 e 0.1159x R 2 = 0.8217 healthy data and building the database, it is important to evaluate whichmathematical models of AI are most appropriate for the problem that one wishes to solve. Then, the chosen models must be implemented using some programming language. A combination of models can also be useful. The results obtained by the algorithm should be analyzed in terms of both the coherence and suitability. These steps are summarized in Figure 2. Why does cardiology need artificial intelligence? The development of AI algorithms has the advantage of not requiring many assumptions in relation to underlying data. 8 Another point is that the nature of these mathematical‑computational models allows, fromobservational data, a high level of evidence due to its high performance, which certainly represents a significant paradigm shift in evidence‑based medicine. It should be noted that traditional clinical trials are generally slow, expensive, time-consuming, and limited in size. 14 In addition, when the database is fed with more (healthy) data, in general, there is an improvement in the performance of the algorithms – which allows the studies to have a continuous character over time. This new archetype can guide the allocation of scarce resources in the health area and facilitate the efficient and accurate identification of decisions that favor the individualization of care based on the flow of information that emerge from an integrated and complex ecosystem: it is a precision medicine. 15,16 Therefore, it can be inferred that the practice of the cardiovascular sciences will have significant impacts, which will translate into a personalized approach and improved outcomes. Basic concepts in artificial intelligence A generic database can be arranged in a matrix of rows and columns. Each line denotes an element from a set of objects to be evaluated according to the same features. Each column, in turn, expresses the values of a given attribute for the various rows in the database and each line represents a lesson to be learned by the mathematical‑computational model. In this way, the term Machine Learning (ML) brings with it a possibility of " learning " from a set of lessons. The term AI is often used interchangeably with the term ML. However, ML is a subset of AI algorithms related the ability of learn from a large amount of data. AI is wider and encompass performing tasks that are normally related to human intelligence such as pattern recognition, problem solving, understanding language or recognizing objects and sounds. 17 It is often said that the types of learning can be: a) Supervised: when the algorithm receives information about each lesson as well as the labels associated with it, having an important role in relation to the prediction. For example, if it is desired to predict whether a patient is more susceptible to cough with the use of angiotensin-converting enzyme inhibitors, analysis should be performed based on a healthy database containing a group of patients that showed such a reaction and another group in which this fact was not observed. b) Unsupervised: when the lesson labels are not provided a priori , it is up to the algorithm to find hidden structures in the database. A hypothetical example is the clusterization of a database of patients with hypertrophic cardiomyopathy according to imaging findings. c) Reinforcement: inspired by behavioral biology, it is a kind of reward-based learning. 18,19 Another important concept is that of cognitive computing. It can be understood as a set of self-learning systems intended to imitate the human thought process based on the use of ML tools, pattern recognition and natural language processing. 9 IBM Watson is an example of cognitive computing in the medical field. 20,21 719