Author Description

"Risk stratification for sudden cardiac death remains one of the foremost challenges in contemporary medicine. Artificial intelligence provides an innovative perspective on modelling the perfect storm that leads to ventricular arrhythmia onset. This thesis investigates the potential of artificial intelligence to enhance risk stratification for ventricular arrhythmias, divided into two main parts. Part One focuses on artificial intelligence-driven personalised risk prediction and seeks to identify opportunities to guide decision-making regarding prophylactic ICD implantation. Part Two explores the potential of artificial intelligence to leverage continuous data streams from digital health technologies for personalised disease monitoring. This thesis shows that the rapid pace of developments in artificial intelligence holds the promise to surpass the accuracy of current risk prediction models andtransform the traditional one-size-fits-all approach into a personalised, case-by-case stratification scheme. However, as we progress, it is crucial to address and overcome the challenges associated with implementing these technologies in clinical settings."--