Commentary on “Performance of a Large Language Model in the Generation of Clinical Guidelines for Antibiotic Prophylaxis in Spine Surgery”

The introduction of artificial intelligence (AI), particularly large language models (LLMs) such as the generative pre-trained transformer (GPT) series into the medical field has her-alded a new era of data-driven medicine. AI’s capacity for processing vast datasets has enabled the development of predictive models that can forecast patient outcomes with remarkable accuracy. LLMs like GPT and its successors have demonstrated an ability to understand and generate human-like text, facilitating their application in medical documentation, patient interaction, and even in generating diagnostic reports from patient data and imaging findings. Over the past 10 years, the development of AI, LLMs, and GPTs has significantly impacted the field of neurosurgery and spinal care as well. 1-5 Zaidat et al. 6 studied performance of a LLM in the generation of clinical guidelines for antibiotic prophylaxis in spine surgery. This study delves into the capabilities of ChatGPT’s models, GPT-3.5 and GPT-4.0, showcasing their potential to streamline medical processes. They suggest that GPT-3.5’s ability to generate clinically relevant antibiotic use guidelines for spinal surgery is commendable; however, its limitations, such as the inability to discern the most crucial aspects of the guidelines, redundancy, fabrication of citations, and inconsistency, pose significant barriers to its practical application. GPT-4.0, on the other hand, demonstrates a marked improvement in response accuracy and the ability to cite authoritative guidelines, such as those from the North American Spine Society (NASS). This model’s enhanced performance, including a 20% increase in response accuracy and the ability to cite the NASS guideline in over 60% of responses, suggests a more reliable tool for clinicians seeking to integrate AI-generated content into their practice. However, the study’s findings also highlight the