Artificial Intelligence in Orthopedic Trauma: A Narrative Review

Research into the applications of artificial intelligence (AI) within orthopedic trauma has undergone an exponential trajectory. While most studies focus primarily on radiographic fracture detection, the scope of AI has rapidly expanded to include preoperative planning, surgical education, and administrative optimization. This narrative review synthesizes evidence using PubMed, OpenEvidence, and Google Scholar to provide a comprehensive overview of AI's current utility and its future implications for the field. Current diagnostic applications represent the most established use of AI, with meta-analyses demonstrating that AI-assisted radiographic interpretation achieves a pooled sensitivity and specificity of 87% and 92%, respectively. Beyond 2D imaging, AI has revolutionized surgical preparation by generating patient-specific 3D anatomical models much faster than traditional manual methods. In the realm of surgical education, AI is transitioning resident assessment from subjective evaluation to a quantitative science by providing objective feedback on metrics like economy of motion. However, while AI can assist in manuscript development and research gap analysis, the risk of "hallucinations" and fabricated citations necessitates rigorous human oversight. Other non-surgical applications offer significant potential for enhancing healthcare equity and clinician well-being. AI-assisted diagnostics have been shown to reduce missed fractures, while "AI scribes" can save surgeons up to an hour of documentation daily. However, despite these advancements, a significant "validation gap" remains. Significant hurdles regarding professional liability, the "efficiency paradox" of increased patient workloads, and the need for multicenter testing must be addressed before AI can be fully integrated into the standard of care. Ultimately, AI represents a transformative shift in orthopedic trauma, moving the field toward a future where clinical decision-making and surgical technique are interwoven with AI assistance to improve patient outcomes and physician well-being.