This narrative review focuses on the clinical applications of artificial intelligence (AI) in musculoskeletal imaging. It discusses various musculoskeletal disorders, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. AI algorithms have been applied to improve fracture detection and classification, assist in bone age assessment, aid in identifying and grading osteoarthritis, and enhance the diagnosis and outcome prediction of bone and soft-tissue tumors. Additionally, AI can help optimize the positioning of orthopedic implants and identify related complications. The review highlights that AI should not replace radiologists but rather complement their work by optimizing workflow, enhancing diagnostic performance, and reducing workload. The increasing number of AI technologies requires radiologists to play a crucial role in selecting and applying these technologies effectively.This narrative review focuses on the clinical applications of artificial intelligence (AI) in musculoskeletal imaging. It discusses various musculoskeletal disorders, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. AI algorithms have been applied to improve fracture detection and classification, assist in bone age assessment, aid in identifying and grading osteoarthritis, and enhance the diagnosis and outcome prediction of bone and soft-tissue tumors. Additionally, AI can help optimize the positioning of orthopedic implants and identify related complications. The review highlights that AI should not replace radiologists but rather complement their work by optimizing workflow, enhancing diagnostic performance, and reducing workload. The increasing number of AI technologies requires radiologists to play a crucial role in selecting and applying these technologies effectively.