Abstract: Machine Learning (ML) systems typically yield definitive outputs, even when the underlying probabilities do not justify a decision. This poses a significant challenge in medical applications, where patients rely on individualized diagnoses, treatments, and prognoses. A recent advancement in ML research addresses this issue by introducing so-called “abstention models,” which enable ML systems to




