ABC | Volume 114, Nº4, April 2020

Review Article Artificial Intelligence in Cardiology: Concepts, Tools and Challenges - “The Horse is the One Who Runs, You Must Be the Jockey” Erito Marques de Souza Filho, 1, 2 Fernando de Amorim Fernandes, 1 Celine Lacerda de Abreu Soares, 1 Flavio Luiz Seixas, 1 Alair Augusto Sarmet M.D. dos Santos, 1 Ronaldo Altenburg Gismondi, 1 Evandro Tinoco Mesquita, 1 Claudio Tinoco Mesquita 1 Universidade Federal Fluminense, 1 Niterói, RJ – Brazil Universidade Federal Rural do Rio de Janeiro - Departamento de Tecnologias e Linguagens, 2 Nova Iguaçu, RJ – Brazil Keywords Artificial Intelligence/trends; Computer Systems/trends; Machine Learning/trends; Cardiovascular Diseases; Clinical Decision-Making. Mailing Address: Erito Marques de Souza Filho • Universidade Federal Fluminense - Departamento de Medicina Clínica – Av. Marques do Paraná, 303. Postal Code 24033-900, Niterói, RJ – Brazil E-mail: mederitomarques@gmail.com Manuscript received December 21, 2018, revised manuscript July 16, 2019, accepted August 28, 2019 DOI: https://doi.org/10.36660/abc.20180431 Abstract The recent advances at hardware level and the increasing requirement of personalization of care associated with the urgent needs of value creation for the patients has helped Artificial Intelligence (AI) to promote a significant paradigm shift in the most diverse areas of medical knowledge, particularly in Cardiology, for its ability to support decision‑making and improve diagnostic and prognostic performance. In this context, the present work does a non-systematic review of the main papers published on AI in Cardiology, focusing on its main applications, potential impacts and challenges. Introduction A person’s everyday life necessitates a huge amount of knowledge about the world and the volume of data in health grows exponentially throughout the world. 1 On the other hand, biomedical knowledge is always expanding in an active and dynamic way and cannot be processed or stored by a single human brain. This situation makes it very difficult for the contemporary physician to keep up-to-date with such a broad spectrum of new data and findings, as well as to use such information easily and in a timely manner. 2 Adding to this framework are the significant burnout rates among health professionals 3,4 and the important impact of medical errors - which in the United States represent the third leading cause of death. 5 This panorama brings with it the need to reorganize the productive structure of health services, associated with various challenges and new perspectives. Given that the current health system is generally unproductive and/or expensive, it is imperative to develop alternative and innovative strategies. The central focus for achieving this goal should be to increase the value for the patient – outcomes reached per dollar spent – so that good outcomes, efficiently obtained, are a target to be pursued. 6 Besides, the recent advances at hardware level related to parallel processing, the existence of several machine-learning methods and the huge amount of annotated data contributed for artificial intelligence (AI) ​to promote a significant paradigm shift in the most diverse areas of medical knowledge and, particularly in Cardiology, for its ability to support decision-making that can improve diagnostic and prognostic performance. These impacts ought to be evaluated from the perspective of patient safety, personalization of care, value creation for the patients, within a scope of technological surveillance – that gradually consolidates AI as fundamental for a medical practice of excellence. 7-11 This scenario makes AI, given its importance, be considered by many as the new electricity. The main journals in cardiology have published reviews in this area and the number of articles on the subject follows a growing trend, as shown in Figure 1 – this behavior is also seen in other medical specialties, such as Neurology. Therefore, the present work performs a non‑systematic review of the main papers published on AI in Cardiology, focusing on its main applications, potential impacts and challenges. The next section presents the conceptual fundamentals on the topic, followed by a discussion on why cardiology needs AI and its main tools. Finally, the main challenges, perspectives and conclusions are presented. What is artificial intelligence? The term AI was used for the first time at the Dartmouth Conference in 1956. 12 Nevertheless, the possibility of machines being able to simulate human behavior and actually think was raised earlier byAlanTuring in1950, whodeveloped a test inorder to differentiate humans frommachines – thus named Turing test. 13 Basically, AI is the product of the combination of sophisticated mathematical models and computation, which allows the development of complex algorithms capable of emulating human intelligence. All this process starts with the construction of a database representative of the problem that one wishes to study – adequately collected and processed– called healthy data. This step is of fundamental importance, as the algorithms will probably not perform well if this prerequisite is not obtained: " garbage in, garbage out ". The nature of these data is quite varied, ranging from socio- environmental, clinical-laboratory, omic-data (e.g., metabolome, proteome, epigenome, lipidome) to information on red, green and blue intensities (RGB system) of each pixel that composes an image, for example. Equally diversified sources of such data include those obtained from electronic medical records or even wearable devices. In this context, the term Big Data is used to describe a huge collection of data for which traditional methods of analysis are unsuccessful in analyzing, searching, interpreting and storing. 9 We highlight the use of these tools in problems of classification, regression, and clusterization. After obtaining 718