Research in artificial intelligence (AI) has progressed over the past decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection a...
Many clinicians remain wary of machine learning because of longstanding concerns about "black box" models. "Black box" is shorthand for models that are sufficiently complex that they are not straightforwardly interpretable to humans. Lack of interpre...
Innovations in health care are growing exponentially, resulting in improved quality of and access to care, as well as rising societal costs of care and variable reimbursement. In recent years, digital health technologies and artificial intelligence h...
Automated interpretation of the 12-lead ECG has remained an underpinning interest in decades of research that has seen a diversity of computing applications in cardiology. The application of computers in cardiology began in the 1960s with early resea...
This paper reviews recent cardiology literature and reports how artificial intelligence tools (specifically, machine learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the underst...
In recent years, numerous applications for artificial intelligence (AI) in cardiology have been found, due in part to large digitized data sets and the evolution of high-performance computing. In the discipline of cardiac electrophysiology (EP), a nu...
Herzschrittmachertherapie & Elektrophysiologie
Jan 15, 2021
Big data and applications of artificial intelligence (AI), such as machine learning or deep learning, will enrich healthcare in the future and become increasingly important. Among other things, they have the potential to avoid unnecessary examination...
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