Title: Artificial Intelligence in the Management of Sports Knee Injuries: A Narrative Review
Author: Saran Singh Gill, Nasir Kharma, Chinmay Madhukar Gupte
Institution: Imperial College London, Imperial College Healthcare NHS Trust, Sportshealing Clinic
Publication Date: The Knee, Vol. 61, Article 104430 (2026)
The Problem: Sports related knee injuries, especially ACL ruptures and meniscal tears, are among the most common and costly setbacks in sport, often leading to chronic pain, instability and early onset osteoarthritis.
Predicting which athletes are at risk, diagnosing tears quickly on imaging, and forecasting graft failure or return to sport timelines depend on weighing dozens of interacting bio-mechanical, physiological and training variables, more than a clinician can reliably track in real time.
This review synthesizes current AI and machine learning applications across that continuum, from pre-injury screening through rehabilitation, and finds that despite strong reported accuracy, most models remain investigational rather than deployment ready.
Methodology: The review pulls together three strands of AI work.
Prediction models (SVM, Random Forest, XGBoost): Act like a risk scoring assistant, screening bio-mechanical, demographic and neuromuscular data to flag athletes before injury occurs, performing strongly for overuse conditions like medial tibial stress syndrome (AUC 0.98) but weaker for ACL rupture in real-world cohorts (AUC 0.63).
Diagnostic CNNs: Function like a tireless second reader, scanning MRI scans to detect and grade ACL and meniscal tears at accuracy approaching radiologist level, especially once combined with clinical variables such as age and inflammatory markers.
Prognostic models (CatBoost, Random Forest): Work like an early warning system for surgeons, using early postoperative strength and balance metrics to predict graft failure, revision risk and 12 month return to sport, with knee hyperextension and quadriceps strength emerging as the most consistent red flags.
Why it Matters:
These tools promise to shift knee care from reactive to proactive, flagging at risk movement patterns before an ACL tear happens and setting realistic, data driven return to play expectations instead of generic timelines.
Most models come from small, single center, often military or elite male cohorts, so predictions may not generalize to female, youth or non elite athletes.
Many are "black box" systems whose reasoning clinicians cannot inspect, which complicates trust and regulatory approval under frameworks like the MHRA, EU AI Act and NICE.
None of the reviewed tools are described as clinically deployable; treat any current AI risk score or return to sport prediction as a decision support input, not a verdict, with a clinician kept firmly in the loop.
ACTIONABLE TAKEAWAYS
Treat AI as one input, not the gate: Weigh AI injury risk and return to sport predictions alongside hop tests, Y-balance scores and clinician judgment, not as a standalone gate for training load or return to play decisions.
Demand population matched validation: Before adopting any vendor tool, ask for external validation data on a population resembling your athletes in sex, age and sport, since most published models are single centre and skew toward elite male cohorts.
Favor explainable models: Prioritize models with published interpretability methods like SHAP over pure accuracy claims, since unexplainable outputs are harder to defend in high stakes return to sport calls.