Bruises can appear when blood vessels rupture, which can lead to the risk of blood leakage into the surrounding tissues. Evaluation and detection of these symptoms, especially those related to health problems or accidents, are very important in medical environments. Bruises can also serve as an alert sign of health problems that a medical evaluation is recommended and might be urgently needed. Unfortunately, it can be very challenging for medical practitioners to appropriately identify and categorize bruises due to the complexity of the situations and many types of bruises. The main goal of this study is to promote the use of Artificial Intelligence (AI) in healthcare systems. It aims to help improve computer-aided practices by making use of an open-source algorithm such as YOLOv8 and incorporate it into a case-based reasoning (CBR) approach for fast and precise identification of bruises. In this study, we introduce an approach to this problem by using the CBR-YOLO approach. The use of the CBR-YOLO approach can help and support the decision-making practice. Although bruises have many types and the same appearance, it can still provide recommendations and commentary on the bruises. This method can be useful in diagnosing patients in a timely manner.