This narrative review explores the clinical applications of artificial intelligence (AI) in musculoskeletal imaging, focusing on areas such as trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implants. AI algorithms have been applied to improve diagnostic accuracy, reduce workload, and enhance efficiency in radiology. In trauma, AI has shown promise in fracture detection and classification, with models achieving performance comparable to trained radiologists. In bone age estimation, AI helps automate workflows and reduce inter-observer variability. For osteoarthritis, AI can assist in identifying and grading abnormalities and predicting disease progression. In bone and soft-tissue tumors, AI, combined with radiomics, may improve diagnosis and outcome prediction. For orthopedic implants, AI can assist in evaluating implant positioning and identifying complications. AI is not intended to replace radiologists but to support them in optimizing workflow, enhancing diagnostic performance, and managing increasing workloads. While AI shows great potential, challenges remain, including the need for diverse datasets and validation across different populations. Future research should focus on improving AI models, ensuring clinical relevance, and integrating AI into routine practice. Radiologists play a key role in selecting and applying AI technologies, ensuring they are used as diagnostic tools rather than replacements. Overall, AI has the potential to significantly enhance musculoskeletal imaging by improving efficiency, accuracy, and patient outcomes.This narrative review explores the clinical applications of artificial intelligence (AI) in musculoskeletal imaging, focusing on areas such as trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implants. AI algorithms have been applied to improve diagnostic accuracy, reduce workload, and enhance efficiency in radiology. In trauma, AI has shown promise in fracture detection and classification, with models achieving performance comparable to trained radiologists. In bone age estimation, AI helps automate workflows and reduce inter-observer variability. For osteoarthritis, AI can assist in identifying and grading abnormalities and predicting disease progression. In bone and soft-tissue tumors, AI, combined with radiomics, may improve diagnosis and outcome prediction. For orthopedic implants, AI can assist in evaluating implant positioning and identifying complications. AI is not intended to replace radiologists but to support them in optimizing workflow, enhancing diagnostic performance, and managing increasing workloads. While AI shows great potential, challenges remain, including the need for diverse datasets and validation across different populations. Future research should focus on improving AI models, ensuring clinical relevance, and integrating AI into routine practice. Radiologists play a key role in selecting and applying AI technologies, ensuring they are used as diagnostic tools rather than replacements. Overall, AI has the potential to significantly enhance musculoskeletal imaging by improving efficiency, accuracy, and patient outcomes.