FractureSight-HCI: A Human-Centered Interactive System for AI-Assisted X-ray Fracture Visualization

Abstract

Fracture detection in X-ray images is a critical task in medical image analysis, where accurate local-ization of suspected fracture regions can support faster, clearer, and more focused clinical interpretation.In this work, we present FractureSight-HCI, a human-centered interactive system for AI-assisted X-rayfracture visualization and clinical decision support. The proposed system combines an image process-ing and deep learning pipeline with an interactive Human–Computer Interaction interface to transformmodel predictions into clear visual outputs that can be reviewed by physicians. The technical compo-nent of the system was developed using a YOLOv8-based object detection model trained on more than20,000 radiographic fracture samples. The training process was conducted using Google Colab and con-tinued for more than three hours, reflecting a practical experimental workflow rather than a simple staticdemonstration. The trained model was evaluated using standard object detection metrics. The preliminary exper-imental results achieved a Precision of 89.5%, Recall of 79.5%, mAP50 of 89.6%, and mAP50-95 of49.3%. These results indicate promising fracture localization capability, especially for identifying suspi-cious regions that require visual clinical review. However, the contribution of this work is not limited tomodel performance. A major focus is the design of an interpretable and physician-centered interface thatdisplays the original X-ray image, presents the AI-assisted fracture localization result, and highlights thesuspected fracture region using a bounding box. This visual presentation reduces ambiguity in the AIoutput and supports a more understandable decision-support workflow. From the HCI perspective, the system was designed according to a human-in-the-loop approach,where the AI model provides visual assistance while the physician remains responsible for the finalmedical decision. The interface supports image selection, visual analysis, highlighted fracture visual-ization, and clinical review in a simplified workflow. By integrating large-scale model training, imageprocessing, object detection, visual explanation, and human-centered interaction, FractureSight-HCIdemonstrates a practical and structured approach for presenting medical AI results in a clinically mean-ingful and user-friendly manner. The proposed system highlights the importance of combining technicalaccuracy with interface clarity, interpretability, and human control in AI-assisted medical imaging ap-plications.