Musculoskeletal radiology is an important tool for the diagnosis of muscle damage, bone fractures, bone tumors, musculoskeletal infection, and other diseases. However, all currently used radiological techniques, including radiography, ultrasonography, computed tomography, and magnetic resonance imaging are associated with their own challenges. With its ability to address these challenges, artificial intelligence (AI) holds the promise to transform a musculoskeletal radiologist’s job in several areas. In the past, AI-based approaches in musculoskeletal radiology were primarily used for measuring bone mineral density or identifying bone tumors. However, recent studies have expanded the application of AI in several other areas, such as image segmentation, resolution enhancement, and fracture identification as well automatic diagnosis of other forms of musculoskeletal damage. This review article discusses numerous older as well as more recent studies to highlight how the development and application of AI-based approaches have evolved in the field of musculoskeletal radiology and how the applicability of these approaches may be improved in the future.
Artificial Intelligence in Musculoskeletal Radiology: Past, Present, and Future
