In the field of pediatric orthopedics, accurate and timely identification of wrist fractures is vital for effective treatment and recovery. Fractures significantly affect daily activities and can lead to long-term health issues. Especially in areas with limited medical resources, or for doctors with less experience, interpreting X-ray images accurately is challenging. This paper introduces the AdvYOLO algorithm, an enhanced version of YOLOv8, trained on the GRAZPEDWRI-DX dataset to diagnose wrist bone pathologies. The integration of the Dilation-wise Residual (DWR) and Large Separable Kernel Attention (LSKA) modules is critical for improving feature extraction and classification, as they allow more effective processing of complex patterns in X-ray images, leading to more accurate diagnostics. The mAP 50 value of AdvYOLO improved from 63.8% to 68.7%, achieving state-of-the-art performance in wrist detection. Additionally, the paper presents BoneVisionAI, a tool to assist doctors, particularly those with less experience, in accurately interpreting children's wrist X-rays, aiming to reduce diagnostic errors in the healthcare sector.