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INTRODUCTION AND OBJECTIVE: Current machine learning (ML) models are limited by poor interpretability, precluding their routine use in planning nerve-sparing at radical prostatectomy (RP). We aimed to leverage explainable artificial intelligence techniques to provide accurate, interpretable, and personalized predictions for side-specific extraprostatic extension (ssEPE).METHODS: A retrospective sample of 900 prostatic lobes (450 patients) from RP specimens at our institution between 2010 and 2020, was used as the training cohort. Features (ie: variables) included patient demographics, clinical, sonographic, and site-specific data from transrectal ultrasound-guided prostate biopsy. The label (ie: outcome) of interest was the presence of ssEPE in the prostatectomy specimen. A ten-fold stratified cross-validation method was performed to train a gradient-boosted model, optimize hyperparameters, and for internal validation. Our model was further externally validated using a testing cohort of 122 lobes (61 patients) from RP specimens at a separate institution between 2016 and 2020. An existing model from the literature which has the highest performance for predicting ssEPE was selected as the baseline model for comparison. Discriminative capability was quantified by area under receiver-operatingcharacteristic (AUROC) and precision-recall curve (AUPRC). Clinical utility was determined by decision curve analysis. Shapley Additive exPlanations were used to interpret the ML model's predictions.RESULTS: The incidence of ssEPE in the training and testing cohorts were 30.7 and 41.8%, respectively. Our model outperformed the baseline model with a mean AUROC of 0.81 vs 0.75 (p<0.01) and mean AUPRC of 0.69 vs 0.60, respectively, on cross-validation of the training cohort. Similarly, our model performed favourably on the testing cohort with an AUROC of 0.81 vs 0.76 (p[0.03) and AUPRC of 0.78 vs 0.72. On decision curve analysis, our model achieved a higher net benefit for threshold probabilities between 0.15 to 0.65. A web application incorporating our model was developed in which deidentified patient data can be inputted to generate an individualized ssEPE prostate map with annotated explanations to highlight which features had the greatest impact on model predictions (www.ssepe.ml).CONCLUSIONS: We have developed a user-friendly application that enables physicians without prior ML experience to assess ssEPE risk and understand the factors driving these predictions to aid surgical planning and patient counselling.