Commentary on “Use of ChatGPT for Determining Clinical and Surgical Treatment of Lumbar Disc Herniation With Radiculopathy: A North American Spine Society Guideline Comparison”

Clinical medicine is a constantly changing field. However, no change is perhaps as drastic as the integration of machine learning (ML) and artificial intelligence (AI) into clinical practice. This rapid adaptation has recently been stretched with the introduction of the chat generative pre-trained transformer (ChatGPT) in 2022. Unlike many other complex tools for ML, ChatGPT is a large language model (LLM) developed with the intent for rapid use by the lay audience. The tremendously low barrier to entry—namely involving generation of an account—has led to expansive interest in the use of ChatGPT in nearly every subfield of surgery, including spine surgery and low back pain. The goal of the study by Mejia et al. 1 was to assess the ability of ChatGPT to provide accurate medical information regarding the care of patients with lumbar disk herniation with radiculopathy. The research team developed a series of questions related to lumbar disk herniation, us-ing the 2012 North American Spine Society (NASS) guidelines as a gold standard. 2 They then collected responses from both ChatGPT-3.5, and ChatGPT-4.0. They quantified several metrics for each response. A response was considered accurate if it did not contradict the NASS guidelines. It was considered overconclusive if it provided a recommendation when the NASS guidelines did not provide sufficient evidence. A response was supplementary if it included additional relevant information for the question. Finally, a response was considered incomplete if it was accurate but omitted relevant information included within the NASS guidelines. Both ChatGPT-3.5 and -4.0 provided accurate responses to just over 50% of questions. Nearly half of all responses were also overconclusive, providing recommendations without direct