Digital Phenotyping of Myocardial Dysfunction With 12-Lead ECG Tiptoeing Into the Future With Machine Learning* (JACC Editorial) – Vol. 76, No. 8, – August 2020

"Skip to content\nHome\nProducts\nClinical\nNews\nInvestors\nAbout Us\nContact Us\nINVEST NOW\nPUBLICATIONS\nDigital Phenotyping of Myocardial Dysfunction With 12-Lead ECG Tiptoeing Into the Future With Machine Learning* (JACC Editorial) – Vol. 76, No. 8, – August 2020\nAugust 8, 2020\n\nThe field of cardiovascular medicine has long been at the forefront of innovation. Innovations in data science, particularly machine learning (ML) and artificial intelligence (AI), have generated enthusiasm in the field to bring about transformative changes to cardiovascular care, replicating the disruption of accepted norms they brought about in other spheres of life (1). However, compared with other industries, the application of these technologies in medicine remains in its infancy with incremental gains in the science supporting their use in clinical care. An area of innovation that has highlighted the promised value of ML and AI in cardiovascular medicine is their application to complex, existing data sources to generate novel insights. This has previously been limited to the automated analysis of imaging data, but now has expanded to data streams previously inaccessible for diagnostic or screening interventions.\n\nLink to article\nDigital Phenotyping of Myocardial Dysfunction With 12-Lead ECG: Tiptoeing Into the Future With Machine Learning? | Journal of the American College of Cardiology (jacc.org)\n