Background: The integration of artificial intelligence (AI) in orthopedic diagnostic imaging represents a paradigm shift in musculoskeletal healthcare, offering enhanced diagnostic accuracy, reduced interpretation time, and improved patient outcomes.
Objective: This study aims to evaluate the current applications, benefits, and challenges of AI implementation in orthopedic imaging modalities including X-ray, MRI, CT, and ultrasound.
Methods: A comprehensive literature review was conducted analyzing AI applications in orthopedic imaging from 2019-2024. We examined machine learning algorithms, deep learning models, and their clinical validation studies across various orthopedic conditions.
Results: AI demonstrates significant improvement in fracture detection (sensitivity 94.2%), osteoarthritis grading (accuracy 89.7%), and spinal pathology identification (specificity 92.1%). Convolutional neural networks showed superior performance in bone tumor classification with 91.3% accuracy compared to traditional methods.
Conclusion: AI integration in orthopedic imaging shows promising results for clinical implementation, though standardization and regulatory considerations remain crucial for widespread adoption.