In neural network models, interpretability can be balanced with high prediction accuracy by incorporating partially-interpretable structures. One approach is to use a framework that combines linear or additive regression methodology with deep learning, creating partially-interpretable neural networks (PINNs). These PINNs allow for statistical inference while retaining high prediction accuracy. By representing the interpretable components of the model using simpler and more transparent functions, such as linear or spline-based representations, it becomes easier to understand the impact of specific predictors on the outcome. This balance between interpretability and accuracy is crucial in applications where understanding the underlying mechanisms driving predictions is essential.