Toward a Patient-Centered, Data-Driven Cardiology

Tele-electrocardiography and bigdata: The CODE (Clinical Outcomes in Digital Electrocardiography) study

“…This huge amount of data -big data, analyzed by methods recently developed in the machine learning and data mining fields, may allow the recognition of hidden patterns that were not detected in the past by traditional statistical methods. This may serve for the development of new analytical tools, opening up a world of new possibilities (5). We hypothesize that a large, annotated, database of digital ECGs, obtained in the community and linked with hospitalizations and death obtained from health or vital records will constitute an electronic cohort able to provide clinically useful prognostic information, as well as a better classification method for standard 12-lead ECG.…”

Application of Neural Networks to 12-Lead Electrocardiography ― Current Status and Future Directions ―

“…The technology has received attention from the medical field because it may extend the utilization of complex medical data beyond the limit of human brain function that is unable to handle high-dimension data. 12 , 13 As expected, the application of AI to medical still-images has been greatly successful, 7 , 14 , 15 but AI has also produced striking results in other tasks such as electronic digitalized health record analysis, 16 , 17 and prediction of clinical outcome from ECG. 1 , 18 – 33 Of note, AI applied to the interpretation of 12-lead ECG has shown a potential beyond the ability of even a well-trained cardiologist, and will be the main focus of this article.…”

2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals

“…Several limitations should be considered and roadblocks removed before AI‐based mHealth strategies become routinely incorporated in clinical practice 436,439,444,459 . Studies on AI are still scarce and based on observational studies and secondary datasets.…”

“…Thus, high‐quality evidence that supports the adoption of many new technologies is not available. Most algorithms work with the "black box" principle, without allowing the user to know the reasons why a diagnosis or recommendation was generated, which can be a problem, especially if the algorithms were designed for a different environment than the one that the current patient is inserted 459,460 Issues regarding cost‐effectiveness, implementation, ethics, privacy, and safety are still unsolved.…”