Artificial intelligence in orthopedic trauma: a comprehensive review
Abdulhamit Misir
Injury. 2025 Jul 1;56(8):112570. doi: 10.1016/j.injury.2025.112570. Online ahead of print.
Abstract
Artificial intelligence (AI) has emerged as a transformative technology in healthcare, with significant applications in orthopedic trauma. This comprehensive review analyzes 217 studies published between 2015 and 2025 to evaluate the current state, applications, and future directions of AI in orthopedic trauma. The field has experienced exponential growth, with 52.5 % of all studies published in 2024 alone. Deep learning approaches (43.3 %) and traditional machine learning methods (39.2 %) dominated the research landscape. Fracture detection (24.4 %) and classification (12.0 %) were the most common applications, followed by prediction (21.2 %) and segmentation (8.3 %). Hip/femur (19.4 %), spine (18.9 %), and wrist fractures (12.0 %) represented the most frequently studied anatomical sites. AI systems frequently matched or exceeded specialist performance in detection and classification tasks, with sensitivities and specificities above 90 % commonly reported. Predictive models for complications and mortality consistently outperformed traditional scoring systems, with improvements in AUC typically between 0.10-0.15. However, only 14.5 % of studies underwent external validation, and just 3.2 % reported prospective clinical validation. Despite remarkable progress in developing accurate AI systems for orthopedic trauma, significant challenges remain in clinical integration, data standardization, and validation across diverse populations. Future development should focus on multimodal approaches integrating diverse data sources, transparent algorithms providing rationales for predictions, and rigorous clinical validation. Point-of-care applications and integration with emerging technologies offer promising directions for clinical impact. As these challenges are addressed, AI has the potential to significantly enhance orthopedic trauma care by improving diagnostic accuracy, optimizing treatment selection, and identifying high-risk patients for targeted interventions.