Generative artificial intelligence (AI) comprises a class of AI models that generate synthetic outputs based on learning acquired from a dataset that trained the model. This means that they can create entirely new outputs that resemble real-world data despite not being explicitly instructed to do so during training. Regarding technological capabilities, computing power, and data availability, generative AI has given rise to more advanced and versatile models including diffusion and large language models that hold promise in healthcare. In musculoskeletal healthcare, generative AI applications may involve the enhancement of images, generation of audio and video, automation of clinical documentation and administrative tasks, use of surgical planning aids, augmentation of treatment decisions, and personalization of patient communication. Limitations of the use of generative AI in healthcare include hallucinations, model bias, ethical considerations during clinical use, knowledge gaps, and lack of transparency. This review introduces critical concepts of generative AI, presents clinical applications relevant to musculoskeletal healthcare that are in development, and highlights limitations preventing deployment in clinical settings.