ABC | Volume 112, Nº4, April 2019

Editorial Ribeiro & Oliveira Cardiology based on data and centered on the patient Arq Bras Cardiol. 2019; 112(4):371-373 Table 2 – Premises to guide the future of artificial intelligence (AI) in medicine • The patient must be considered to be at the center upon implementation of any new technology. • The incorporation of these new technologies for diagnosis and treatment should occur after robust validation of their clinical efficacy. • The use of digital tools and decision algorithms by patients should be another option for those patients who feel empowered. • Cross-disciplinary training will need to be undertaken involving healthcare professionals, engineers, computer scientists, and bioinformaticians, who will minimize the difficulties of implementing the new technology. Adapted from Topol EJ 16 Table 1 – Examples of recent studies with artificial intelligence (AI) applications implemented in cardiology 8-13 Article Publication Application of AI in cardiology Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study 8 Dawes TJW et al. MR imaging study Radiology 2017;283(2):381-90 Evaluation of outcomes in pulmonary arterial hypertension based on a highly accurate algorithm derived from nuclear magnetic resonance Differences in repolarization heterogeneity among heart failure with preserved ejection fraction phenotypic subgroups 9 Oskouie SK et al Am J Cardiol 2017;120(4):601–6 Identification of phenotypic patterns in heart failure with preserved ejection fraction and unfavorable prognosis Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram 10 Attia ZI Nat Med. 2019 Jan;25(1):70-74 AI applied to electrocardiography for identification of patients with left ventricular dysfunction Artificial intelligence to predict needs for urgent revascularization from 12-lead electrocardiography in emergency patients 11 Goto S et al PLoS ONE 201914(1):e0210103 Prediction of urgent revascularization in patients with chest pain in the emergency room Fast and accurate view classification of echocardiograms using deep learning 12 Madani, A..et al NPJ Digit. Med. 2018 1, 6,.24 Use of AI for interpretation with good accuracy of echocardiograms Fully automated echocardiogram interpretation in clinical practice feasibility and diagnostic accuracy 13 Zhang, J. et al. Circulation 2018 138, 1623–35 Automated assessment of echocardiographic measurements comparable to or greater than manual assessment the high expenditures in this sector. Topol, 16 in a recent review, emphasized the premises that should guide the future application of AI in healthcare (Table 2). 16 If greater availability of data and new AI techniques allow for more accurate diagnoses and prognoses, as well as personalized treatments, various aspects of healthcare practice will continue to depend on other dimensions, such as political, economic, and cultural ones, and the ability of healthcare professionals to interact with patients and the community. The issue of unequal access to healthcare is still critical in Brazil and in developing countries, and requires large investments to improve the organization of the healthcare system. Even when healthcare services and evidence-based guidelines are available, for common and relevant conditions such as hypertension and diabetes, the implementation gap is gigantic and best practices are not absorbed by healthcare professionals, or recommended measures are not implemented by patients and their families. The implementation science developed in recent decades, proves to be as important as the data science for the recognition of bottlenecks hindering the complete use of preventive and therapeutic measures ensuring benefit to the patients, who may live more and better, benefiting from all available knowledge. 17 Thus, personalized medicine and AI promise to provide a powerful tool for complex and personalized healthcare data management, which will only be effective if used in the context of the art of caring and the doctor-patient relationship, allowing a new paradigm of medicine based on data but focused on the patient. Physicians and healthcare professionals will be responsible for evaluating and learning the new techniques, expanding the resources available to fully benefit the patients, in terms of not only their physical condition, but also their mental and spiritual conditions, minimizing the suffering that results from the process of illness. 18 1. Sackett DL, Straus SE, Richardson WS, Rosenberg W, Brian Haynes R. Evidence-Based Medicine: How to practice and teach EBM. 2nd ed. London: Churchill Livingstone; 2000. 2. Tonelli MR. Integrating evidence into clinical practice: an alternative to evidence-based approaches. J Eval Clin Pract. 2006;12(3):248-56. 3. Kernick DP. Lies, damned lies, and evidence-based medicine. Lancet. 1998;351(9118):1824. 4. Gu D, Li J, Li X, Liang C. Visualizing the knowledge structure and evolution of big data research in healthcare informatics. Int J Med Inform. 2017 Feb;98:22-32. References 372