Background and Objective: Medical imaging is fundamental in evaluating musculoskeletal injuries. The recent expansion of artificial intelligence (AI), especially in deep learning techniques, has led to new possibilities in image-based diagnostics. This review explores current AI-driven methods used in orthopedic trauma imaging and discusses their relevance in clinical settings. Scope of Review: The review encompasses studies from 2015 to 2025 that apply AI tools to the interpretation of X-rays, computed tomography (CT), and magnetic resonance imaging (MRI) in the context of fractures, ligament tears, and joint-related damage. It also includes predictive systems and decision-support technologies. Findings: AI solutions demonstrate notable performance in trauma detection, sometimes reaching diagnostic levels comparable to radiology experts. Deep neural networks are particularly effective in identifying soft tissue injuries in MRI, such as anterior cruciate ligament damage. Implementation is still hindered by insufficient clinical trials, data limitations, and the absence of universal methodological frameworks. Conclusions: AI applications in orthopedic imaging show strong promise. However, broader clinical adoption depends on further validation, methodological standardization, and effective integration into healthcare workflows.
Hander et al. (Mon,) studied this question.
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