The Performance of ChatGPT-4o and DeepSeek-R1 in Interpreting Thyroid Nodule Ultrasound Text Report: A Multicenter Study (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Clinicians exhibit considerable variability in diagnosing and managing thyroid nodules. While large language models (LLMs) show promise in processing medical data, their effectiveness and reliability in standardizing the interpretation of thyroid nodule ultrasound text report have yet to be thoroughly validated. </sec> <sec> <title>OBJECTIVE</title> To assess two LLMs, DeepSeek-R1 and ChatGPT-4o, in interpreting thyroid nodule ultrasound text report, emphasizing the accuracy in benign-malignant differentiation, the agreement of Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) classification and management recommendation, and the stability of each task. </sec> <sec> <title>METHODS</title> We analyzed 1,063 ultrasound text reports from three medical centers, with 306 nodules confirmed by histopathology. Each nodule's report was processed through two LLMs using standardized prompts, repeated five times, with the final result determined by mode voting. </sec> <sec> <title>RESULTS</title> DeepSeek-R1 excelled over ChatGPT-4o in differentiating benign from malignant nodules, with superior sensitivity (0.879 vs. 0.692), accuracy (0.729 vs. 0.644), and Area Under the Curve (AUC) (0.694 vs. 0.632). However, senior radiologists achieved notably better results with higher accuracy (0.804), and AUC (0.865) compared two LLMs. In C-TIRADS classification, DeepSeek-R1 also outperformed ChatGPT-4o (κ=0.770 vs. κ=0.688, Δκ=0.083 [95% CI: 0.048, 0.122]). Both models showed substantial agreement with clinicians on management recommendation (κ=0.606 vs. κ=0.608, Δκ=-0.002 [95% CI: -0.044, 0.041]). In terms of stability, LLMs exhibited almost perfect agreement in C-TIRADS classification (α=0.864 vs. α=0.866, Δα=-0.003 [95% CI: -0.023, 0.017]) and management recommendation (κ=0.853 vs. κ=0.849, Δκ=0.004 [95% CI: -0.026, 0.033]). However, in benign-malignant discrimination, DeepSeek-R1 demonstrated significantly greater stability than ChatGPT-4o (κ=0.849 vs. κ=0.550, Δκ=0.260 [95% CI: 0.191, 0.321]). </sec> <sec> <title>CONCLUSIONS</title> Our study highlights the potential of LLMs for interpreting thyroid nodule ultrasound text reports. DeepSeek-R1 outperformed in benign-malignant differentiation accuracy and classification consistency, whereas ChatGPT-4o and DeepSeek-R1 performed similarly in management recommendation. </sec>

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