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.

The aim of this research was to identify the 50 articles most frequently referenced concerning patellofemoral instability (PFI) and to analyze their features. A search was performed in the Thomson ISI Web of Science using keywords such as “patellofemoral instability,” “patellar instability,” “patellar dislocation,” and “patella luxation.” This research included all publications related to PFI, covering aspects such as diagnostic and both nonoperative and operative treatment. The citation counts for the 50 articles ranged from 165 to 1024 citations. Notably, the top ten articles received a minimum of 348 citations each. In total, 84% (n = 42) of the studies were clinical, while the remainder consisted of basic science investigations (including three anatomical and five biomechanical studies). The predominant level of evidence was IV, accounting for 32%. The American Journal of Sport Medicine was responsible for publishing 34% of these articles. Most of the research took place in the United States and twelve additional countries. The years when the most-referenced papers were published spanned from 1985 to 2020, with the 2000s representing the highest share of articles (74%), and the years between 2006 and 2010 showing the peak quantity of articles (n = 15). This article provides a building block in the PFI management. The selection of these articles is useful for learning more about current trends on PFI and anticipating future developments.