ABC | Volume 114, Nº4, April 2020

Marques et al. Artificial Intelligence in Cardiology Arq Bras Cardiol. 2020; 114(4):718-725 Review Article Figure 2 – Main Illustration. Remind: Remind: “Garbage in garbage out” “GPU can be useful” Choice of mathematical model (BIG) Healthy Data Real Problem SVM, NB, KM, DAM, AG, RF, ... K-means, AM (perceptron, RBF, deep learning, convolutional learning...)... Other architetures and other models “The horse is the one who runs, you must be the jockey” C7: Data-Based Care C6: dealing with errors C5: the african proverb C1: Misuse C2: improve math knowledge C3: get healthy data Challenges C4: get security Implementation Analysis of results, consistency and adequacy of the models laboratory data, clinical data (EHR), omics-data, image data other sources data: wearable devices, IOT... it the fundamental role of mathematics and computation in this currently ongoing revolution. This revolution will bring unimaginable possibilities in medical practice, such as the construction of quality phenomappings - ML models developed with the aim of clustering patients in function of their large mass of phenotypic characteristics in order to facilitate the decision‑making process. 57 Thus, it is necessary that these competencies are stimulated early, mainly with a focus on solving problems related to the reality for which one wishes to promote improvements. This will certainly be reflected in a need to reformulate the cardiovascular contents (and why not say, medical content in general) of undergraduate and postgraduate courses in Medicine: a passive or merely expositive education, with an extensive load and that prioritizes the capacity of the student's memory seems, more and more, to be inadequate, as one realizes that Medicine must be a space for creativity and value generation. c) Challenge 3 – get healthy data: the use of healthy data is of fundamental value for the success of the algorithms. Thus, it is required that health units encourage their health professionals regarding the thoroughness at the level of data filling/obtaining as well as maintaining any data sources, from forms, electronic medical records, image data or even unconventional data, such as those obtained byMedina et al. 58 - who developed a successful Online Social Networks Health tool in which the patient himself anonymously inserts health monitoring information, including physiological data, daily activities, emotional states, and interaction with others patients. 58 Therefore, data management becomes as important as other routine behaviors in evidence‑based medicine, such as proper handwashing or even the use of a defibrillator during cardiac arrest. In this way, the formation of multidisciplinary data teams and the constant training of the teams assume a primordial role. It is noteworthy that much of the slowness and difficulty that some health units have in usingMLmodels is tied to absent or incipient healthy data. d) Challenge 4 – get security: the advent of these tools brings with it a fundamental concern with data security, to a level never before experienced, as access to such data by unauthorized persons can lead to catastrophic consequences for both health institutions and the patients. The creation of a security team plays an important role in this new process. The General Data Protection Regulation represents an advance in this direction. Blockchain and its variants are important tools that can improve security substantially. 722