Artificial intelligence (AI) is one of the largest technological revolutions medicine has faced. One of the pioneers in its integration into clinical and potential clinical workflows is the field of radiology. When applied to the subspecialty of musculoskeletal radiology, AI has been shown to help improve workflow efficiency, diagnosis, and interventions across the discipline. The main AI models responsible for this swift improvement are machine learning, deep learning, and radiomics for diagnostic purposes and natural language processing and large language models for reporting processes. While the benefits and pull factors of AI are clearly strong and display reliably impressive results, they must be interpreted in light of their limitations. There is some debate about the ethical questions surrounding AI regarding data privacy and accountability. This chapter aims to discuss the key methods of AI integration in musculoskeletal radiology as well as the main drawback and limitations.