This study highlights the use of a machine learning model (MLM) that integrates radiographic imaging and clinical data to predict whether treatment is needed for patients with distal radius fractures (DRFs). The researchers retrospectively collected radiographs and clinical data from 1,139 adult patients with DRFs and trained MLMs with the patient data. One model used only the images and the other combined the images with clinical data (age, BMI, gender, etc.). The models were evaluated for predictive performance using accuracy, sensitivity, specificity, predictive value, and area under the curve (AUC) from the receiver operating characteristic (ROC). Grad-CAM and SHAP analyses were also utilized to better interpret model predictions. The models integrating imaging and clinical data outperformed the image-only model, achieving an accuracy of 92.98%, sensitivity of 93.28%, and specificity of 92.55%. However, the AUC difference was not statistically significant. Grad-CAM visualizations indicated that the models used the radiocarpal joint, volar, and dorsal cortex of the radial metaphysis, indicating that these regions are important for surgical prediction. SHAP analysis identified female gender and the presence of concomitant or subsequent fractures to be important predictors for surgical need. The study concluded that integrating radiographic imaging and clinical data improves model performance in predicting DRF surgical need and can be a useful tool for supporting surgical decision-making in orthopedics with AI.