This article presents a standardized reporting framework for AI studies in orthopedic surgery, addressing the surge in AI applications such as image interpretation, outcome prediction, and robotic surgical planning. The authors synthesize recommendations from 17 established AI reporting frameworks, emphasizing the need for transparency and reproducibility. Key elements include clear documentation of dataset origins, annotation processes, and data splitting for training, validation, and testing. The framework also requires detailed reporting of model architecture, hyperparameters, preprocessing steps, and evaluation metrics like accuracy and AUC. Model explainability is highlighted through tools like Grad-CAM and SHAP, alongside full methodological transparency, including failed experiments and source code sharing. The article provides orthopedic-specific examples and a practical checklist to guide implementation, aiming to enhance research integrity and foster clinical adoption of AI tools.