This study seeks to augment the reliability of being able to discern between ruptured and intact rotator cuff tendons in ultrasound diagnostics through the means of integrating a Convolutional Block Attention Module into the framework of YOLOv7, introducing the YOLOv7-CBAM variant. The study went off a dataset of 280 patients, who were categorized into two groups, torn or intact, according to ultrasound and MRI imaging. Both the YOLOv7 and YOLOv7-CBAM models were then trained using transfer learning techniques. The models were appraised across a multitude of categories, namely accuracy, precision, sensitivity, F-1 score, among others, with Grad-CAM applied to visualize regions of interest. In consonance with the results, YOLOv7-CBAM surpassed the original model, exhibiting superior performance across all the aforementioned categories. By the same token, the Grad-CAM analysis accentuated that the model primarily zeroed in on identifying defects within the tendon. Upon utilizing the YOLOv7-CBAM model, clinical physicians observed a notable uptick in accuracy (from 80.86% to 88.86%), accompanied by a large-scale improvement in interobserver reliability. This AI-assisted model certainly has considerable scope if integrated into clinical workflows, helping to enhance diagnostic precision and consistency across healthcare providers.