## 1 Introduction  
Fractures are a common occurrence in childhood due to high levels of physical activity and ongoing skeletal development. Children engage in activities such as climbing, running, and sports, increasing the risk of injuries including fractures. Epidemiological studies indicate that approximately 42% of boys and 27% of girls sustain at least one fracture before the age of 16 [1]. These injuries present unique challenges to healthcare providers due to the presence of growth plates (physes), which are areas of developing cartilage tissue near the ends of long bones. Growth plates are crucial for longitudinal bone growth but also introduce complexities in fracture healing and potential complications. One significant complication that can arise following long bone fractures in children is fracture overgrowth [2]. Unlike adults, where fractures typically heal to restore the bone's original length and structure, children's bones can exhibit accelerated growth during the healing process. This accelerated growth can lead to the injured limb becoming longer than its uninjured counterpart, resulting in limb length discrepancies (LLDs) and angular deformities. These discrepancies can have profound effects on a child's gait, posture, and overall quality of life, potentially leading to functional impairments and psychological distress [3]. The unpredictable nature of fracture overgrowth adds complexity to pediatric -fracture management. Traditional predictors, such as the child's age, the type of fracture, and its location, provide only general guidance and often lack the precision needed for individualized risk assessment. This unpredictability makes early detection and effective management challenging, and in many cases, corrective surgeries such as epipbysiodesis or limb lengthening procedures become necessary. These interventions carry inherent risks and can significantly impact the child's life, both physically and emotionally. In the face of these challenges, there is a growing interest in leveraging advanced technologies to improve outcomes in pediatric fracture care. Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has emerged as a powerful tool in healthcare. AI systems can analyze vast amounts of complex data, identifying patterns and making predictions that may not be apparent through traditional analysis. In fields such as radiology, pathology, and adult orthopedics, AI has transformed predictive modeling and personalized treatment strategies. Supervised learning algorithms, including random forests, support vector machines, and neural networks, have been successfully applied to predict outcomes and complications in various medical conditions [4; 5; 6]. Given the potential of AI to revolutionize various aspects of medicine, its application in predicting and managing pediatric fracture overgrowth is a logical and promising next step. By integrating clinical data, imaging findings, genetic information, and treatment details, AI could provide more accurate risk assessments and facilitate early interventions tailored to individual patients. This could not only improve clinical outcomes but also reduce the burden on healthcare systems and families [7].  
This comprehensive review aims to explore the intersection of AI and pediatric fracture overgrowth. We will delve into the current understanding of fracture overgrowth, examining its epidemiology, pathophysiology, and clinical implications. We will discuss the emergence of AI in medicine and orthopedics, highlighting key technologies and applications. Furthermore, we will examine existing evidence and potential applications of AI specific to fracture overgrowth and discuss ethical considerations, challenges, and future research directions. By synthesizing the existing literature, we hope to shed light on how AI can transform the management of pediatric fractures and stimulate further research and innovation in this critical area.