Evaluating Large Language Models for Decision Support in Minimally Invasive Spine Surgery Triage and Procedural Categories.

ABSTRACT: Study DesignVignette-based cross-sectional study.ObjectiveGenerative artificial intelligence (AI) programs such as large language models (LLMs) are reshaping treatment decision-making, yet applications in minimally invasive spine surgery (MISS) are still scarce. This study examines whether OpenAI's ChatGPT-5 Pro and Google's Gemini 2.5 Pro reproduce expert management categories from published MISS cases and measures agreement at procedural and binary triage levels.MethodsWe constructed 90 clinical vignettes from published case reports and prompted each LLM to assign 1 or more of ten predefined categories with two-sentence rationales. Agreement with reference was assessed using Jensen-Shannon divergence (JSD), Stuart-Maxwell tests, Cohen's κ, and McNemar's test for surgical vs non-surgical

SUBMITTER: Kartal A 

PROVIDER: S-EPMC12722155 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature