# Applications of Natural Language Processing Tools in Orthopaedic Surgery: A Scoping Review
## 1 Introduction  
Natural language processing (NLP) refers to a subfield of artificial intelligence (AI) technology that includes generative AI tools that are designed to utilise large language models to receive, rationalise and subsequently generate human language [1; 2]. These technologies have gained significant popularity, attributed to widely accessible and advanced iterations including ChatGPT, a generative pre-training transformer (GPT) chatbot tool released by OpenAI (San Francisco, CA, USA), Bing Chat by Microsoft (Redmond, WA, USA) and BARD by Google (San Francisco, CA, USA). These tools have led to a paradigm shift in the ways we can approach digital problems. Specifically, the underlying algorithms of these popular tools have been extensively trained to receive, recognise and interpret input from human language. In doing so, these tools have the ability to generate output data for various applications including content creation, education, restructuring or re-organisation of data as well as provide contextually relevant answers to prompts in real time. Popular ways this has been utilised have been to use Chatbot NLP tools such as ChatGPT or Google Bard to answer frequently asked questions, provide frameworks for academic assignments, and even author academic research papers. The advanced computational ability of these tools to process big data and generate natural human language has therefore understandably seen global implementation of these technologies across multiple industries including education, finance and business.  
Progressive digitalisation of healthcare in the field of orthopaedic surgery, which utilises large amounts of data-based infrastructure through electronic medical records, telehealth, intra-operative stereotaxis and the use of radiology therefore offers a valuable opportunity for implementing these tools to improve the delivery of healthcare for orthopaedic surgeons. To date, the use of natural language processing tools in orthopaedic surgery is limited, with minimal research into clinical application of these tools and therefore no guidelines as to how orthopaedic surgery can effectively implement these technologies. Despite this, the translatability of generative AI and NLP tools is significantly high given the vast amount of unstructured free text in progress notes, operative notes, radiology reports and pathology reports. There is a growing field of research into the ways in which NLP tools can be applied in orthopaedic surgery to improve the experience for all stakeholders including surgeons, researchers, nursing and allied health staff as well as patients. In particular, there is a potential for these tools to enhance auditing processes, research capabilities, prognostication and triaging as well as optimisation of healthcare delivery for surgeons and healthcare access for patients. To date, however, these outcomes are poorly characterised, with no current studies that have explored the potential of NLP tools within the global journey of orthopaedic surgery. Furthermore, the challenges of implementing these technologies in practice, including issues related to privacy, clinician acceptance, resources and funding, as well as appropriate stewardship of data, remain poorly considered.  
The main objective of this scoping review, which has been conducted in accordance with the Arksey and O'Malley framework, is to provide an up-to-date review of the current landscape of research exploring the potential avenues of implementation for NLP in orthopaedic surgery. This review aims to answer the research question of how NLP tools can be utilised to streamline processes within orthopedic surgery by characterising the efficacious evidence-based methods in which NLP tools can be applied through the journey of orthopaedic surgery. This review also aims to further contribute key insights and challenges of integrating contemporary NLP technologies into orthopaedic practice.