• Ahmad K Almekkawi, James P Caruso, Soummitra Anand, Angela M Hawkins, Rayaan Rauf, Mayar Al-Shaikhli, Salah G Aoun, and Carlos A Bagley.
• Saint Luke's Marion Bloch Neuroscience Institute Department of Neurosurgery, Kansas City, Missouri, USA. Electronic address: akmakkawi@gmail.com.
• World Neurosurg. 2024 Dec 23; 194: 123531123531.

ObjectiveThis study aimed to investigate the accuracy of large language models (LLMs), specifically ChatGPT and Claude, in surgical decision-making and radiological assessment for spine pathologies compared to experienced spine surgeons.MethodsThe study employed a comparative analysis between the LLMs and a panel of attending spine surgeons. Five written clinical scenarios encompassing various spine pathologies were presented to the LLMs and surgeons, who provided recommended surgical treatment plans. Additionally, magnetic resonance imaging images depicting spine pathologies were analyzed by the LLMs and surgeons to assess their radiological interpretation abilities. Spino-pelvic parameters were estimated from a scoliosis radiograph by the LLMs.ResultsQualitative content analysis revealed limitations in the LLMs' consideration of patient-specific factors and the breadth of treatment options. Both ChatGPT and Claude provided detailed descriptions of magnetic resonance imaging findings but differed from the surgeons in terms of specific levels and severity of pathologies. The LLMs acknowledged the limitations of accurately measuring spino-pelvic parameters without specialized tools. The accuracy of surgical decision-making for the LLMs (20%) was lower than that of the attending surgeons (100%). Statistical analysis showed no significant differences in accuracy between the groups.ConclusionsThe study highlights the potential of LLMs in assisting with radiological interpretation and surgical decision-making in spine surgery. However, the current limitations, such as the lack of consideration for patient-specific factors and inaccuracies in treatment recommendations, emphasize the need for further refinement and validation of these artificial intelligence (AI) models. Continued collaboration between AI researchers and clinical experts is crucial to address these challenges and realize the full potential of AI in spine surgery.Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

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