Artificial intelligence in the management of sports knee injuries: a narrative review.
Clinical Snapshot
PICO Framework
| P — Population | Athletes and individuals sustaining sports-related knee injuries, including ACL rupture, overuse injuries, and those undergoing surgical reconstruction or rehabilitation |
| I — Intervention | Artificial intelligence (AI) and machine learning (ML) models applied across the sports knee injury continuum — injury prediction, diagnostic imaging interpretation, clinical workflow integration, and postoperative/rehabilitation outcome modelling |
| C — Comparator | Conventional clinical assessment, standard imaging interpretation, and traditional rehabilitation outcome prediction methods (implicit comparator; not formally defined) |
| O — Outcomes | Injury prediction accuracy (AUC), diagnostic imaging performance, graft failure and revision surgery prediction, return-to-sport prediction, and rehabilitation outcome modelling |
Bottom Line
This narrative review from Imperial College London surveys the emerging landscape of AI and machine learning applications across the sports knee injury continuum, encompassing injury prediction, diagnostic imaging, clinical workflow integration, and rehabilitation outcome modelling. The authors report that some predictive models have achieved AUC values exceeding 0.90 in pilot settings for ACL rupture risk identification, and that machine learning approaches show promise for predicting graft failure and return-to-sport outcomes. However, the review's conclusions must be interpreted cautiously. As a narrative review without a registered protocol, formal inclusion criteria, risk of bias assessment, or statistical synthesis, it is susceptible to selection and publication bias and sits at a relatively low level of evidence on the CEBM hierarchy. The authors themselves acknowledge that most AI tools remain investigational, lack external validation, and are trained on narrow, unrepresentative datasets. For Australian clinicians, no AI-based knee injury management tools are currently TGA-approved or MBS-reimbursed. This review is best read as a structured horizon-scanning exercise rather than actionable clinical guidance. Practice change is not warranted on the basis of this evidence alone. Clinicians should await prospective, multicentre validation studies and regulatory-grade evaluations before integrating AI tools into sports knee injury management pathways.
Key Findings
P Value: Not reported at the review level
Effect Size: Predictive models for ACL rupture risk and overuse injury identification have achieved AUC values above 0.90 in some experimental and pilot studies; specific pooled effect sizes are not reported
Primary Outcome: AI and machine learning model performance across sports knee injury prevention, diagnosis, prognosis, and rehabilitation outcome prediction
Nnt Or Sensitivity: Individual study-level sensitivity, specificity, and AUC values are referenced narratively but not pooled; no NNT or summary diagnostic accuracy statistics are calculable from this review
Confidence Interval: Not reported — no meta-analytic synthesis performed
Clinical Application
Current AI tools described in this review are predominantly investigational and not yet clinically deployable at scale. Implementation would require validated, explainable models integrated into existing clinical information systems, with appropriate regulatory approval and clinician training. Feasibility in routine clinical practice remains limited at this time. No AI-based tools for sports knee injury management are currently listed on the Australian Register of Therapeutic Goods (ARTG) as approved medical devices for clinical decision support in this domain. The TGA's Software as a Medical Device (SaMD) framework would apply to any AI diagnostic or prognostic tool seeking Australian market approval. The RACGP and Sports Medicine Australia have not yet issued specific guidelines on AI-assisted knee injury management. Medicare Benefits Schedule (MBS) items do not currently include reimbursement for AI-assisted musculoskeletal assessment. Australian sports medicine practitioners should monitor TGA SaMD guidance updates and emerging RACGP position statements as this field develops. The diverse athletic population in Australia — including elite, community, and Indigenous athletes — underscores the importance of the representativeness concerns raised by the authors. Competitive and recreational athletes sustaining sports-related knee injuries, particularly those at risk of ACL rupture, undergoing ACL reconstruction, or engaged in postoperative rehabilitation. Potentially applicable to sports medicine physicians, orthopaedic surgeons, physiotherapists, and sports scientists involved in athlete management.
Abstract
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.
References
- 1.Gill, S. S., Kharma, N., & Gupte, C. M. (2026). Artificial intelligence in the management of sports knee injuries: a narrative review. The Knee. https://doi.org/10.1016/j.knee.2026.104430
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