Computer-Aided Diagnosis (CAD) systems are vital in medical image analysis, assisting clinicians in interpreting complex imaging data to diagnose conditions such as cancer. However, while traditional CAD systems excel at detecting these patterns, they often struggle to follow a line of reasoning needed to draw inferences. This gap has led to growing interest in neurosymbolic AI. By integrating neural and symbolic approaches, neurosymbolic AI—especially through frameworks like Logic Tensor Networks (LTNs)—has the potential to enhance CAD systems by making them more robust and interpretable. This advancement addresses key issues of reliability and transparency, which are crucial in high-stakes fields like healthcare.