Introduction The last decade has seen a rapid increase in the use of artificial intelligence (AI) approaches such as machine learning and deep learning in the sport sciences. However, despite the increased interest in this area, the scope and utility of, and challenges associated with, these approaches are relatively unknown. Methods This critical review aimed to scan sport science research for articles using AI in athlete development contexts (i.e., talent/athlete identification, talent/athlete selection, and talent/athlete development). Through database, external reference lists, book chapters, and other relevant resource searching, information from eligible articles was extracted and form the basis of the current review. Key takeaways The use of AI was prominent in three main areas: improving athlete assessment, athlete selection and classification, and athlete development and training. These technologies have been used in a variety of ways and appear to have potential value for those working in this area. The challenges associated with these approaches are also discussed. Conclusion AI in the context of athlete development allows for access to more data, more easily, and with greater statistical complexity, than ever before. Importantly, a balanced approach that embraces both innovation and critical evaluation seems necessary to ensure these tools enhance, rather than disrupt, the athlete identification, selection, and development landscape.
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