Artificial intelligence (AI) and deep learning have multiple potential uses in aiding the musculoskeletal radiologist in the radiological evaluation of orthopedic implants. These include identification of implants, characterization of implants according to anatomic type, identification of specific implant models, and evaluation of implants for positioning and complications. In addition, natural language processing (NLP) can aid in the acquisition of clinical information from the medical record that can help with tasks like prepopulating radiology reports. Several proof-of-concept works have been published in the literature describing the application of deep learning toward these various tasks, with performance comparable to that of expert musculoskeletal radiologists. Although much work remains to bring these proof-of-concept algorithms into clinical deployment, AI has tremendous potential toward automating these tasks, thereby augmenting the musculoskeletal radiologist.
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