Introduction: Sports-related knee injuries are common and debilitating, often leading to chronic pain, early osteoarthritis, and reduced performance.Artificial Intelligence (AI) has emerged as a promising tool to improve their prevention, diagnosis, prognosis, and rehabilitation.This review summarises current evidence on the clinical applications, limitations, and future directions of AI and machine learning in sports-related knee injuries.Methods: A narrative review of PubMed, Embase, Medline and Web of Science was conducted, examining recent literature on AI-based models in musculoskeletal and sports medicine.The review was categorised into key domains: injury prediction and prevention, diagnostic imaging performance, AI-enabled clinical workflows, alongside postoperative and rehabilitation outcome modelling.Results: AI algorithms demonstrate strong potential across the sports knee injury continuum.Predictive models analysing biomechanical and physiological data have achieved high area under the curve (AUC) values, in some cases above 0.90, in experimental and pilot setting when identifying athletes at risk of ACL rupture or overuse injuries, while machine learning approaches have been used to predict graft failure, revision surgery, and return-to-sport.However, most remain investigational rather than clinically deployable, with limited explainability, insufficient external validation, and training datasets that are often narrow or unrepresentative of broader athletic populations.Conclusion: AI has the potential to transform the management of sports-related knee injuries through more predictive, personalised, and precise care.However, wider clinical adoption will require multicentre validation, improved interpretability, and robust ethical and regulatory oversight.With further development, AI may enhance injury prevention, recovery, and improve long-term joint health outcomes in athletes.
Gill et al. (Fri,) studied this question.