# Applications of Natural Language Processing Tools in Orthopaedic Surgery: A Scoping Review
## 4 Discussion  
As healthcare continues its shift towards digitalisation, the potential for leveraging technology to enhance the orthopaedic surgery experience becomes evident for all involved parties, from surgeons to patients and allied health staff. Yet, the realm of orthopaedics lags in a structured, guideline-based approach to integrating these digital tools, with limited research exploring their specific application into streamlining and augmenting processes in orthopaedics. As orthopaedics becomes further digitalised, with advancements like electronic medical records, the capabilities of NLP and generative AI tools stand out. These technologies, with their vast computational power and adaptability, hold the promise of optimising efficiency, task streamlining, and auditing processes. This review aims to answer this research question pertaining to how we can best utilise these AI tools by presenting a comprehensive overview of existing research on the use of NLP and generative AI within orthopaedic surgery. Furthermore, it sheds light on potential future research trajectories, aiming to inform the creation of evidence-based strategies and frameworks for NLP technology adoption in this field. Our analysis encompassed and synthesised the current traits, evidence, and potential of NLP and generative AI tools from 24 publications, focusing on their influence in the realm of orthopaedics. A key finding is the current absence of evidence-based guidelines for these tools' application within orthopaedics. There has been an emerging body of research since 2018 that started to explore the strategies by which implementation of NLP could improve processes within orthopaedic surgery. Notably, the majority of this research hals from the United States, the birthplace of popular tools like ChatGPT and Google Bard. For implementation, our scoping review identified one study by [PERSON] et al. that assessed the use of NLP for intraoperative monitoring and guidance in spinal surgery [3]. They found that NLP could with high accuracy, as compared to manual chart review, retrospectively identify important events including changes in patient status and baseline signals, stability and change in neuromonitoring signals. Practically these early findings could establish a role for NLP in retrospectively identifying and evaluating events that occur during surgery for learning and auditing processes. However, the true potential lies in augmenting these NLP systems by integrating them into modern monitoring tools to potentially develop a real-time system that can identify important intraoperative changes to support more efficient and rapid decision making for orthopaedic surgeons. This same concept could be applied to other orthopaedic surgeries, such as hip arthroplasties where monitoring the sciatic or femoral nerves can have significant importance to patient outcomes. Our review pinpointed burgeoning research into NLP's applications for diagnostic purposes, specifically for identifying adverse events linked to orthopaedic surgery and assisting in pinpointing orthopaedic diagnoses. Six studies probed the deployment of NLP for these tasks, leveraging its capability to extract data from diverse digital documents such as radiology reports, consultation notes, and pathology reports [4; 5; 6; 7; 8; 9]. In all cases, NLP is able to correctly identify relevant adverse outcomes including hip dislocations, periprosthetic joint and surgical site infection and intraoperative vascular injury. Furthermore, five studies explored the use of NLP in assisting with the diagnostic process for orthopaedic patients [10; 11; 12; 13; 14]. In these studies, NLP was able to extract data from radiology reports of multiple modalities to accurately define orthopaedic diagnoses including meniscal tears, bone metastases, periprosthetic fractures and traumatic orthopaedic injuries. Interestingly, one study could perform this task effectively from non-English radiology reports [11].