Current trends and future prospects of language models and processing systems in spine surgery - a scoping review

Natural language processing (NLPs) and Large language models (LLM), such as ChatGPT, represent transformative advancements in artificial intelligence (AI). Their implementation into the medical field has a broad potential, and this review discusses the current trends and prospects of NLPs and LLMs in spine surgery, assessing their potential benefits, applications, and limitations. The methodology involved a comprehensive narrative review of existing English literature related to the use of NLPs and LLMs in spine surgery. We searched the databases PubMed, EMBASE, Web of Science and Scopus from inception until 16th June 2025 using keywords evolving around LLM, natural language processing and spine surgery. Original studies, clinical reports, and case series were included, while abstracts or unpublished studies were excluded. From 221 initial records, 37 studies were included: 18 evaluated LLMs and 19 evaluated NLP-based tools. LLMs were commonly used for clinical decision-making (n = 8), patient counseling (n = 7), classification (n = 2), and in research (n = 1). NLPs were applied in classification tasks (n = 12), clinical decision-making (n = 3), patient counseling (n = 1), postoperative opioid monitoring (n = 2), and research registry development (n = 1). ChatGPT-4 achieved up to 92% accuracy in clinical recommendations, outperforming GPT-3.5 in multiple tasks. Comparative analyses have found that newer versions of LLMs, such as ChatGPT-4, outperform previous versions, evident by greater accuracy and to a lesser extent of artificial hallucination. However, limitations persist, including overconfident outputs, adherence gaps to clinical guidelines, and inconsistent patient readability. While this review suggests that NLPs and LLMs can have a significant impact on spine practice, it is important to keep their limitations in mind and implement them with caution. To maximize the benefits of these models in spine surgery, future research should focus on improving model sensitivity and specificity, promoting multi-disciplinary collaborations, and addressing ethical considerations regarding the use of language models in medical practice, including the inherent issue of hallucination of these models.