Artificial Intelligence for the Diagnosis and Management of Patellofemoral Instability: A Comprehensive Review

Patellofemoral instability (PFI) is a multifactorial orthopedic condition affecting predominantly young and active individuals. Accurate diagnosis and personalized treatment planning remain challenging due to the complex interplay of anatomical and biomechanical factors. Recently, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has gained attention for its role in musculoskeletal imaging and orthopedics care. This review explores the current and potential applications of AI in diagnosis and management of PFI. A total of 11 relevant articles were identified and included in the review. Articles originated from six countries, with China having the most contributions (n = 4), followed by Finland (n = 3), and Korea, Japan, USA and Portugal with 1 each. In the results section, findings are grouped into three themes: (A) Diagnosis, (B) Outcomes and Complications and (C) Challenges, Limitations and Future Directions. The review also discussed advancements in automated image analysis, predictive modeling and outcome prediction. Overall, AI has the potential to improve consistency, efficiency, and personalization of care in patients with PFI, although still requiring technological developments for implementation in daily practice. Existing studies are limited by small datasets, methodological heterogeneity, and lack of external validation. Future research should focus on multicenter data integration, explainable AI frameworks, and clinical validation to enable translation into routine orthopedic practice.