How To Verify The Memory Loss Of A Machine Learning Model

It is a known fact that deep learning models get better with diversity in the data they are fed with. For instance, data in a use case related to healthcare data will be taken from several providers such as patient data, history, workflows of professionals, insurance providers, etc. to ensure such data diversity. These data points that are collected through various interactions of people are fed into a machine learning model, which sits remotely in a data haven spewing predictions without exhausting. However, consider a scenario where one of the providers ceases to offer data to the healthcare project and later requests to delete the provided information. In such a case, does the model remember or forget its learnings from this data?