Artificial intelligence for fracture diagnosis in orthopedic X-rays: current developments and future potential

Abstract

The use of artificial intelligence (AI) in the interpretation of orthopedic X-rays has shown great potential to improve the accuracy and efficiency of fracture diagnosis. AI algorithms rely on large datasets of annotated images to learn how to accurately classify and diagnose abnormalities. One way to improve AI interpretation of X-rays is to increase the size and quality of the datasets used for training, and to incorporate more advanced machine learning techniques, such as deep reinforcement learning, into the algorithms. Another approach is to integrate AI algorithms with other imaging modalities, such as computed tomography (CT) scans, and magnetic resonance imaging (MRI), to provide a more comprehensive and accurate diagnosis. Recent studies have shown that AI algorithms can accurately detect and classify fractures of the wrist and long bones on X-ray images, demonstrating the potential of AI to improve the accuracy and efficiency of fracture diagnosis. These findings suggest that AI has the potential to significantly improve patient outcomes in the field of orthopedics.