Artificial Intelligence in Orthopedic Trauma Surgery: Current Applications, Challenges, and Future Perspectives
Keywords:
artificial intelligence; machine learning; orthopedic trauma; fracture detection; robotic surgery; deep learning; surgical navigationAbstract
Artificial intelligence (AI) has rapidly emerged as a transformative technology in orthopedic trauma surgery, reshaping diagnostic and therapeutic approaches. This review synthesizes recent literature on AI applications in fracture detection, classification, outcome prediction, and robotic-assisted surgery. Deep learning models for fracture detection achieve diagnostic accuracy comparable to or exceeding experienced radiologists, with success rates reaching 85-95% across various anatomical sites. Machine learning prediction models demonstrate superior performance in stratifying surgical risk compared to traditional scoring systems. Robotic-assisted surgery with AI-guided navigation reduces operative time by 25% and intraoperative complications by 30%, while improving implant positioning accuracy by 40%. Despite substantial clinical promise, challenges persist including limited prospective validation, data standardization, and integration into routine clinical workflows. This review emphasizes the critical need for rigorous external validation and standardized training protocols before widespread clinical adoption. Future directions include multimodal data fusion, real-time decision support, and personalized treatment planning.
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