There is now emerging evidence that AI may support diagnostics in electrophysiology by automating common clinical tasks or aiding complex tasks using deep neural networks that are superior to currently implemented computerised algorithms.4 Soon, AI simulations of the circuit of monomorphic ventricular tachycardia may be used to guide catheter ablation, or even stereotactic radioablation for a vast number of patients.5 Combining data obtained from several diagnostic modalities using AI might elucidate pathophysiological mechanisms of new, rare, or idiopathic cardiac diseases, aid the early detection or targeted treatment of cardiovascular diseases or allow for screening of disorders currently not associated with the ECG.4 Is all this the future or just wishful thinking? Rebecca Goldin, writing for the Genetic Literacy Project in response to President Obama’s 2013 announcement of a broad new research initiative to understand the human brain, provides perspective:7 “The human brain is estimated to have approximately 86 billion neurons (8.6 x 1010), each neuron with possibly tens of thousands of synaptic connections; these little conversation sites are where neurons exchange information. Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia.