Bridging the Gap Between Artificial Intelligence Research and Clinical Practice in Cardiovascular Science: What the Clinician Needs to Know

Abstract

by the CHA 2 DS 2 VASc score. 5 More widespread use has the potential to improve patient-centred care by further individualising a patient’s level of risk, thus enabling the management of modifiable risk factors. An added benefit would be the ability to account for the dynamic nature of risk in certain cardiovascular outcomes. For example, ML and the use of mobile health data could enable stroke risk prediction to adapt to treatment changes over time and incident risk factors, in contrast with the static nature of current standard risk scores. 5 the explosion creation currently. methods of enabling improvement in performance of ML models. prediction of including AF and as supraventricular ectopic beat and to better use of of