Artificial Intelligence in Orthopedic Trauma: Current Applications, Clinical Evidence, and Future Directions
Keywords:
artificial intelligence; deep learning; fracture detection; orthopedic trauma; machine learning; surgical robotics; outcome prediction; radiomicsAbstract
Artificial intelligence (AI) has rapidly become a transformative force in orthopedic trauma, reshaping how fractures are detected, classified, and managed. This review synthesizes current applications, clinical evidence, and emerging challenges across the trauma care pathway. Deep convolutional neural networks now identify fractures on radiographs with accuracy approaching that of expert clinicians, while machine learning models predict postoperative mortality and complications from routinely collected clinical data. Robotic and navigation systems augment surgical precision and substantially reduce intraoperative radiation exposure. Despite exponential research growth, with the majority of studies published within the last two years, clinical translation remains limited by inadequate external validation, data heterogeneity, and barriers to workflow integration. We summarize comparative performance of major AI methods, outline an integrated clinical workflow, and discuss the regulatory, ethical, and infrastructural prerequisites for responsible adoption. AI is best understood as an augmentation of clinical judgment rather than a replacement, with realistic value contingent on rigorous prospective validation.
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