AI-driven Technologies for Wrist Fracture Prediction: A Narrative Review of Emerging Approaches

Wrist fractures account for approximately 18% of all fractures and are especially common in older adults with osteoporosis and in younger patients following high-energy trauma. Predicting healing outcomes in these cases remains clinically challenging due to variability in fracture types, patient-specific factors, and treatment pathways. Although artificial intelligence (AI) systems have already demonstrated diagnostic accuracies exceeding 95% in detecting and classifying wrist fractures on radiographs, their use in prognostic modeling is still emerging. This narrative review examines recent developments in AI-driven approaches aimed at improving clinical prognosis following wrist fractures. Advanced models-such as convolutional neural networks (CNNs), transformers, and hybrid architectures-can identify subtle imaging and clinical features associated with complications like malunion, delayed healing, or nonunion. The integration of multimodal data, including comorbidities, imaging, and even osteogenomic profiles, shows promise in enhancing risk stratification and guiding more personalized follow-up strategies. Emerging technologies such as explainable AI, synthetic data generation, and federated learning offer potential solutions to challenges related to data availability, interpretability, and model generalization across care settings. Despite encouraging results, further validation in real-world clinical environments and standardization of outcome definitions are needed. In summary, AI-based prognostic tools for wrist fractures could support orthopedic decision-making by identifying high-risk patients early, tailoring follow-up protocols, and improving long-term outcomes through more individualized care.