Ventricular fibrillation (VF) occurs in ~10 % of myocardial infarction (MI) patients. These patients have higher in-hospital mortality and increased risk to lethal arrhythmias. The susceptibility for VF during acute MI and the underlying mechanisms remain incompletely understood. Utilizing patient-specific computational heart models to investigate the underlying causes could give a unique insight into VF during MI in a non-invasive manner. The objective of this study is to automate the process of generating patient-specific heart models and utilize them to identify risk factors for VF during first acute MI before and after primary percutaneous coronary intervention (PPCI). 38 patients (17 VF and 21 non-VF) underwent MRI scans five days and three months post-MI. Finite element models were constructed by segmenting MRI scans into healthy and infarcted tissue, modeled as a gradient of ischemia 15 minutes post-occlusion with decreased conduction and altered action potential morphology. Furthermore, all five-day models were paced from 17 sites in the left ventricle to simulate ectopic activity and assess arrhythmia inducibility. We successfully implemented an efficient semi-automated pipeline for constructing the finite element models, available open-source to the public. Furthermore, the pipeline was utilized to generate the most extensive collection of personalized ventricular models created so far. By analyzing the results, the five-day models had a significantly larger scar than the three-month models (8 % vs. 2,6 %, p-value less than 0,05). Through simulations, we found that all inducible sites had reentrant circuits initiated and persisted within the ischemic zone. The border zone was particularly susceptible to arrhythmias, where 44 % or border zone pacing sites resulted in reentry. Furthermore, the patients that were inducible had significantly larger infarcts (12,06 % vs. 1,96 %, p-value less than 0,05) than the non-inducible patients.