  Rights provisioned within data protection regulations, permit patients to
request that knowledge about their information be eliminated by data holders.
With the advent of AI learned on data, one can imagine that such rights can
extent to requests for forgetting knowledge of patient's data within AI models.
However, forgetting patients' imaging data from AI models, is still an
under-explored problem. In this paper, we study the influence of patient data
on model performance and formulate two hypotheses for a patient's data: either
they are common and similar to other patients or form edge cases, i.e. unique
and rare cases. We show that it is not possible to easily forget patient data.
We propose a targeted forgetting approach to perform patient-wise forgetting.
Extensive experiments on the benchmark Automated Cardiac Diagnosis Challenge
dataset showcase the improved performance of the proposed targeted forgetting
approach as opposed to a state-of-the-art method.
