Publication date: Jan 11, 2025

In clinical practice, distinguishing between spinal tuberculosis (STB) and spinal tumors (ST) poses a significant diagnostic challenge. The application of AI-driven large language models (LLMs) shows great potential for improving the accuracy of this differential diagnosis. To evaluate the performance of various machine learning models and ChatGPT-4 in distinguishing between STB and ST. A retrospective cohort study. 143 STB cases and 153 ST cases admitted to , from January 2016 to June 2023 were collected. This study incorporates basic patient information, standard laboratory results, serum tumor markers, and comprehensive imaging records, including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), for individuals diagnosed with STB and ST. Machine learning techniques and ChatGPT-4 were utilized to distinguish between STB and ST separately. This study encompassed a cohort of 143 cases diagnosed with STB and 153 cases diagnosed with ST. Six distinct machine learning models, along with ChatGPT-4, were employed to evaluate their differential diagnostic effectiveness. Among the six machine learning models, the Gradient Boosting Machine (GBM) algorithm model demonstrated the highest differential diagnostic efficiency. In the training cohort, the GBM model achieved a sensitivity of 98. 84% and a specificity of 100. 00% in distinguishing STB from ST. In the testing cohort, its sensitivity was 98. 25%, and specificity was 91. 80%. ChatGPT-4 exhibited a sensitivity of 70. 37% and a specificity of 90. 65% for differential diagnosis. In single-question cases, ChatGPT-4’s sensitivity and specificity were 71. 67% and 92. 55%, respectively, while in re-questioning cases, they were 44. 44% and 76. 92%. The GBM model demonstrates significant value in the differential diagnosis of STB and ST, whereas the diagnostic performance of ChatGPT-4 remains suboptimal.

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