Hughes, Jonathan Daniel

Abstract Acute, traumatic meniscus tears are common and should be repaired whenever possible. However, the biological age of the patient, along with the specific tear morphology, may significantly influence the selection of the repair technique, expected clinical outcomes and the postoperative rehabilitation protocol. Risk factors for failure of meniscus repair, such as ligament laxity, need to be addressed to improve the healing potential of the meniscus. Biologic augmentation strategies, including bone marrow stimulation, platelet‐rich plasma, fibrin clot, mesenchymal stem cells or meniscal wrapping, can be used to enhance healing and reduce failure of meniscus repair rates. However, the clinical evidence remains limited and of low quality. As such, the routine use of biologic augmentation in meniscus repair warrants critical evaluation. In patients with post‐meniscectomy syndrome, meniscus allograft transplantation (MAT) may be a viable option to reduce pain and joint degeneration and improve knee function. Meniscus implants provide alternatives to traditional MAT; however, their long‐term efficacy, chondroprotective effects and integration within the knee joint require further research. Meniscus reconstruction using tendon autografts has been described as an alternative treatment option in patients with meniscus loss. Emerging methods, such as gene therapy and repurposed drugs, demonstrate in vitro potential for advancing meniscal repair, although their clinical applications are still in their infancy. Future research is necessary to optimize meniscal repair techniques, enhance clinical efficacy, and ultimately, improve outcomes in patients. Level of Evidence Level V.

Abstract Artificial intelligence (AI) encompasses the development of systems that can perform human‐like tasks, such as treatment guidance, decision‐making, pattern recognition and understanding language. Within AI, machine learning and deep learning play pivotal roles in diagnosis and outcome prediction, while natural language processing aids in synthesising large datasets from the electronic medical record. In orthopaedics, AI has demonstrated success in various areas, including image evaluation, surgical planning, outcome prediction, cohort identification and administrative tasks. The purpose of this manuscript was to provide an overview of the benefits of AI implementation within the field of orthopaedics. An additional goal was to address the challenges associated with producing high quality AI‐based research in a rapidly developing field. Level of Evidence Level IV.