Classification of Aortic Arch Anatomy into Normal, Variant, and Pathological Categories: Evaluation of a Large Language Model’s Classification accuracy

Background: Large language models are increasingly integrated into medical education and diagnostic support, yet their capacity to interpret anatomically complex, variation-rich vascular structures remains underexplored.This study evaluated the performance of ChatGPT-5.1 Plus in classifying aortic arch configurations as normal, variant, or pathological. Materials and Methods:Thirty-nine text-based anatomical descriptions derived from authoritative references were used; each description was presented in an independent session with a standardized few-shot prompt requiring structured output.The procedure was repeated after one week to assess temporal consistency.Accuracy was measured across different categories, and statistical agreement was calculated using Cohen's kappa.Results: The model demonstrated excellent accuracy in identifying normal anatomy (100%) and high performance in recognizing anatomical variants (91.7%).However, pathological patterns were classified with lower accuracy (70.6%) because anomalies were misinterpreted as benign variations.Overall agreement with the reference standard was substantial (Kappa = 0.766; p < 0.001).In a binary evaluation comparing normal or variant anatomy with pathological cases, the model achieved an accuracy of 84.62%, a sensitivity of 70.59%, and a specificity of 95.45%.Five of the six were undercalls of pathology.Conclusion: ChatGPT-5.1 Plus shows significant promise in recognizing standard aortic arch anatomy and common variants.However, its reduced sensitivity to atypical or clinically significant anomalies underscores the necessity for expert oversight when it is used for educational or classification purposes in specialized medical contexts.