As tools derived from artificial intelligence are used more frequently in medicine and health-related domains, understanding their predictions becomes increasingly important when determining the trustworthiness of a prediction. 
As the goal of this project is to interpret a neural network, it requires two main phases: the evaluation of the model and the interpretation of the model. The success of the first phase required the implementation of generalised methods to evaluate neural networks according to pre-established metrics. Once the model's quality is determined through the first phase, it is possible to implement the interpretation techniques that allow a human user to understand and analyse the model's predictions, thus concluding the second phase of the project.
The project's third and final phase was comprised of the analysis of the interpretation data obtained from the new methods and the presentation of the results to the other people working on this same neural network.