EDITORIAL
From the Department of Orthopaedic Surgery at Rush University Medical Center in Chicago, Illinois.
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Large Language Models in Spine Surgery: Useful Tool or Overhyped Assistant?
Aimen A. Khan, BS
Would you upload your medical records to ChatGPT? In a recent national survey of U.S. adults, 77% of respondents said they are concerned about the privacy ramifications of sharing health information w ith artificial intelligence (AI). Despite these concerns, 41% report uploading personal medical information into an AI chatbot.1 As large language models (LLMs) become increasingly integrated into healthcare, their growing use by patients and physicians alike demands a closer examination. Tools such as Gemini, Claude, ChatGPT, and other models have showcased an ability to generate human-like text, synthesize and condense information into sizable portions, and answer questions as a personal assistant for a variety of tasks. As these technologies become increasingly accessible, the interest in analyzing their application to spine surgery continues to grow. The question is not whether LLMs can be used in spine surger y, but where they provide meaningful value and where their limitations become clinically significant. W hile some view them as transformative technologies that will fundamentally change
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clinical practice, others see them as overhyped chatbots whose capabilities have been exaggerated.
What Are LLMs and How Do They Work? LLMs are prediction engines, generating responses by identifying patterns in enormous amounts of text and predicting the next likely word in a sequence. This allows LLMs to present human-like language and flow. 2 However, vital recognition is that these systems do not truly understand the information they generate and are not producing answers through the thought process of a physician, but rather statistical relationships learned during training. A defining limitation is “hallucination,” in which responses will sound highly convincing, even providing citations, while offering completely incorrect or fabricated information. This distinction has also been demonstrated in benchmark studies, including evaluations that LLMs can achieve passing marks on standardized medical examinations such as the U.S. Medical Licensing Examination (USMLE).3 However, further analysis shows that
Maximillian Lee, BS
Noah A. Pogonitz, BS
Daniel K. Park, MD
Kern Singh, MD
Vertebral Columns Summer 2026