ABC | Volume 114, Nº4, April 2020

Marques et al. Artificial Intelligence in Cardiology Arq Bras Cardiol. 2020; 114(4):718-725 Review Article e) Challenge 5 – need for collaboration (the African proverb): there is an African proverb that says, " if you want to go fast go by yourself, but if you want to go far go with many ". This applies a lot to this data environment: collaboration between institutions allows the construction of huge healthy databases (Big Data), which tends to favor the performance of ML algorithms. f) Challenge 6 – dealing with errors: one important issue concerns the errors of AI models. It is inadequate to believe that such models are error-free. It may, for example, be the result of overfitting or occurring by using unhealthy data - which make the results unreliable. However, the practice has shown high performance in several applications. These models are probabilistic, and it is always desirable that their errors be minimal. This scenario has clinical implications, for example, an AI model that predicts with 99% probability that a patient has a greater propensity than the general population to have cardiac myocarditis or amyloidosis. There is a probability, although small, that this will not occur, and that the procedure adopted by the cardiologist is inadequate. In that case, the question is who can be held accountable in these cases? Is it appropriate? Should the patient sign a consent form in these cases? Certainly, the solution includes robust regulation of the use of these tools and strengthening of a new type of relationship: physician‑patient-data. g) Challenge 7 – data-based care management: while ML's tools follow an inexorable path, on the other hand, several healthcare professionals remain fearful about these tools because of its possible ability to replace physicians in their tasks. However, when the history of Medicine is remembered, it is worth mentioning, for example, that the appearance of automated machines to perform the whole blood count did not replace the hematologist, but rather resulted in a greater speed of the work process and allowed the professional to be able to act in other important issues in the specialty. The central idea is to provide better support for decision‑making, including better performance. It is data‑driven care management with a high dynamism and constant updating - which will promote greater personalization of care 59 and a real-time evaluation of the experience of the health system users, aiming at generating value for the patient. In this context, the mechanical tasks will be substitutable and a diversity of new tasks will be included into the routine of the cardiologist of precision, from the adequate construction of the databases to the critical reflection on the results obtained by the mathematical-computational models, as well as the development of an adequate physician-patient-data relationship. Therefore, there is a migration of human skills as well as the expansion of their capabilities from the emergence of new tools, which should be part of the technical arsenal of the 21 st century cardiologist. This panorama allows us to compare ML models to a horse and doctors to jockeys: " the horse is the one who runs , you must be the jockey ". Conclusions AI, in fact, has been shown to be a fundamental tool for the clinical practice of current cardiology. Several applications have been successfully performed and have allowed significant improvements from a diagnostic and therapeutic point of view and in relation to personalized care. To be able to use such tools, it is imperative that healthy data be used, which certainly implies a new design in the modus operandi of many health services. The nature of these data is varied and includes new sources, such as wearable devices and omic-data. On the other hand, this new digital ecosystem requires an acquisition of knowledge not traditionally found in regular medical courses. Therefore, a curricular redesign is required and ought to be object of a profound debate and specific actions. On the other hand, the entire panacea brought by AI is not free from challenges such as: the ethics limits of its use, the necessity of improving math knowledge, the building of an ecosystem that ensure high levels of security and confidentiality for the patients, the acquisition of healthy data, the needs of expand the physician-patient-data association, the necessity of collaboration and the data-based care management. In this context, the cardiologist-jockey (or physicians in general) must be a protagonist of changes and has to replace an eventual fear of the tools by a greater involvement with the objective of generating value for the care. It is important to keep in mind possible challenges and obstacles to be overcome and to maintain an engagement and critical sense in the search for solutions: "the horse is the one who runs, you must be the jockey". Author contributions Conception and design of the research: Souza Filho EM, Seixas FL, Santos AASMD, Gismondi RA, Mesquita ET, Mesquita CT; Acquisition of data: Souza Filho EM, Fernandes FA, Soares CLA, Santos AASMD; Analysis and interpretation of the data: Souza Filho EM, Fernandes FA, Soares CLA, Seixas FL, Gismondi RA, Mesquita ET, Mesquita CT; Writing of the manuscript: Souza Filho EM, Fernandes FA, Soares CLA, Gismondi RA, Mesquita ET; Critical revision of the manuscript for intellectual content: Seixas FL, Santos AASMD, Mesquita CT. Potential Conflict of Interest No potential conflict of interest relevant to this article was reported. Sources of Funding There were no external funding sources for this study. Study Association This article is part of the thesis of Doctoral submitted by Erito Marques de Souza Filho, from Universidade Federal Fluminense. Ethics approval and consent to participate This article does not contain any studies with human participants or animals performed by any of the authors. 723